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AbstractNear-infrared (NIR) spectroscopy system is frequently used in food production because of its superior characteristics. Establishing models on spectral data for prediction has always been of interest. However, no satisfactory model has been developed on different data types for accuracy and reliability. In this study, an approach strategy is reported for the calibration of NIR spectra by utilizing a combination of the bootstrap technique and an ensemble method. The approach comprises three steps. First, data are resampled to create bootstrap samples. Second, four calibration models are applied to the grouped data: partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural network (BPNN), and principal component analysis-backpropagation neural network (PCA-BPNN). Finally, predictions obtained after the calibration are combined using the ensemble method. The data studied include 215, 32, and 540 samples, which were characterized as hyperspectral, small sample, and categorical data, respectively. Root mean square error of prediction (RMSEP), R squared (R2), and residual prediction deviation (RPD) were used to describe the accuracy. Coverage probability of the prediction interval (PICP) and normalized average prediction interval width (NMPIW) were utilized to evaluate the reliability. The accuracies of the methods exhibited the following order: the proposed method > PLSR > SVR > BPNN ≈ PCA-BPNN. The results confirm that the reported model achieved satisfactory accuracy and was more reliable than the single calibration models. [ABSTRACT FROM AUTHOR]

Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for measuring rice's protein content, demands considerable manpower, time, and costs, including preprocessing such as removing the rice husk. Therefore, of the technology is needed to rapidly and nondestructively measure the protein content of paddy rice during harvest and storage stages. In this study, the nondestructive technique for predicting the protein content of rice with husks (paddy rice) was developed using near-infrared spectroscopy and deep learning techniques. The protein content prediction model based on partial least square regression, support vector regression, and deep neural network (DNN) were developed using the near-infrared spectrum in the range of 950 to 2200 nm. 1800 spectra of the paddy rice and 1200 spectra from the brown rice were obtained, and these were used for model development and performance evaluation of the developed model. Various spectral preprocessing techniques was applied. The DNN model showed the best results among three types of rice protein content prediction models. The optimal DNN model for paddy rice was the model with first-order derivative preprocessing and the accuracy was a coefficient of determination for prediction, Rp² = 0.972 and root mean squared error for prediction, RMSEP = 0.048%. The optimal DNN model for brown rice was the model applied first-order derivative preprocessing with Rp² = 0.987 and RMSEP = 0.033%. These results demonstrate the commercial feasibility of using near-infrared spectroscopy for the non-destructive prediction of protein content in both husked rice seeds and paddy rice. [ABSTRACT FROM AUTHOR]

Phenolic compounds constitute an essential part of the plant's secondary metabolites and play a crucial role in ecosystem functioning, including nutrient cycling and plant defence against biotic and abiotic stressors. Quantifying the phenolic compounds across global biomes is important for monitoring the biological diversity and ecosystem processes. However, our understanding of foliar phenolic compounds remains limited, particularly regarding how they vary among temperate tree species and whether their variation and absorption features can be assessed using spectroscopy at the leaf level. In this study, we examined the relationships between the spectral properties of fresh leaves from temperate tree species and two ecologically important phenolic compounds (i.e., total phenol and tannin). We sampled the leaves of four dominant tree species (i.e., English oak, European beech, Norway spruce, and Scots pine) across two European temperate forest sites. Continuum removal was applied to the leaf spectra to enhance the assessment of the subtle absorption features that correlate with the phenolic content. Total phenol and tannin concentrations were estimated by comparing the performance of two empirical methods, namely partial least squares regression (PLSR) and Gaussian processes regression (GPR). Our results showed a large range of variation in total phenol and tannin between temperate tree species (p < 0.05). Spectral analysis revealed persistent and distinct phenolic absorption features near 1666 nm in the spectra of English oak, Norway spruce and European beech, whereas Scots pine exhibited a weaker absorption feature near 1653 nm. Regression results showed that both PLSR and GPR accurately estimated total phenol and tannin across temperate tree species, with informative bands for predicting these two traits well-corresponded between the two models utilised. Our results also suggested that total phenol was overall more accurately predicted than tannin regardless of employed methods. The most accurate estimations were achieved using PLSR with the continuum-removed SWIR spectra (total phenol: R 2=0.79, NRMSE=9.95%; tannin: R 2=0.59, NRMSE=14.53%). Testing the models established for individual species or forest types revealed variability in their prediction performances, with these specific models demonstrating lower accuracy (R 2=0.47–0.69 and 0.34–0.54 for total phenol and tannin, respectively) compared to the cross-species model. Our study extends the understanding of absorption features of phenolic compounds in common temperate tree species and demonstrates the potential for a generalised spectroscopy model to predict foliar phenolic compounds across temperate forests. These findings provide a foundation for mapping and monitoring phenolic compounds in temperate forests at the canopy level using airborne and spaceborne imaging spectroscopy. [ABSTRACT FROM AUTHOR]

Excessive total nitrogen (TN) content in topsoil is a major cause of eutrophication when nitrogen flows into water systems from soil losses. Therefore, TN content prediction is essential for establishing topsoil management systems and protecting aquatic ecosystems. Recently, hyperspectral imaging (HSI) has been used as a rapid, nondestructive technique for quantifying various soil properties. This study developed a machine and deep learning-based model using hyperspectral imaging to rapidly measure TN contents. A total of 139 topsoil samples were collected from the four major rivers in the Republic of Korea. Visible-to-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging data were acquired in the 400–1000 nm and 895–1720 nm ranges, respectively. Prediction models for predicting the TN content in the topsoil were developed using partial least square regression (PLSR) and one-dimensional convolutional neural networks (1D-CNNs). From the total number of pixels in each topsoil sample, 12.5, 25, and 50% of the pixels were randomly selected, and the data were augmented 10 times to improve the performance of the 1D-CNN model. The performances of the models were evaluated by estimating the coefficients of determination (R2) and root mean squared errors (RMSE). The Rp2 values of the optimal PLSR (with maximum normalization preprocessing) and 1D-CNN (with SNV preprocessing) models were 0.72 and 0.92, respectively. Therefore, HSI can be used to estimate TN content in topsoil and build a topsoil database to develop conservation strategies. [ABSTRACT FROM AUTHOR]

Understanding the origins and hotspots of suspended sediments is crucial for safeguarding the biotic communities and for management of water quality in river‐reservoir systems. This study is an attempt to develop a new analytical framework to deconvolve the suspended sediment sources contribution using a novel index (i.e., erosion susceptibility index, ESI). The ESI is computed using sediment fingerprinting information, sediment yield, and the areal coverage of the sediment sources (land use). We have used X‐ray diffraction (XRD) and Partial least square regression (PLSR) based spectral fingerprinting technique to apportion the land use classes as sources of sediments in a representative catchment of Chotanagpur Plateau (CP), India (Konar reservoir catchment). PLSR model on the XRD spectra has performed well with R2 > 0.88 and identified agricultural areas as the major contributor of suspended sediments by delivering 28%–43%, varying seasonally (in the period of 2018–2019). However, based on the ESI values barren lands and human settlements were found to be the most crucial land use classes (with highest ESI value of 1.31 and 1.08 respectively) in terms of sediment generation (22%–29% and 14%–23% respectively) with only 14% and 12% areal coverage. The results have demonstrated that the state of critical conservation urgency of different land uses can be quantified more effectively when combining spectral tracers based knowledge with ESI. Plain Language Summary: It is crucial to identify the areas where soil erosion is most prevalent in order to reduce the amount of sediments carried by runoff into the reservoir. One method used to identify the most significant sedimentary contributions into a watershed is sediment fingerprinting. The purpose of this research is to improve our comprehension of sediment dynamics by using a new spectral fingerprinting technology (i.e., XRD‐PLSR) to classify sediment sources according to land use and further use this knowledge to develop a novel index (Erosion Susceptibility Index) to enhance the understanding on sediment dynamics more critically. The primary result of this research is a categorization of land uses according to their conservation priority for mitigating reservoir sediment inputs. Key Points: Effective X‐ray powder diffraction spectral fingerprinting identified agricultural land use as the primary sediment sourceLinked sediment source apportionment and source weightage through a novel indexErosion Susceptibility Index (ESI) has shown the state of critical conservation urgency is more for barren lands than agricultural areas [ABSTRACT FROM AUTHOR]

Abstract Background At present, the conventional methods for determining photosynthetic products of microalgae are usually based on a large number of cell mass to reach the measurement baseline, and the result can only reveal the average state at the population level, which is not feasible for large-scale and rapid screening of specific phenotypes from a large number of potential microalgae mutants. In recent years, single-cell Raman spectra (SCRS) has been proved to be able to rapidly and simultaneously quantify the biochemical components of microalgae. However, this method has not been reported to analyze the biochemical components of Cyclotella cryptica (C. cryptica). Thus, SCRS was first attempt to determine these four biochemical components in this diatom. Results The method based on SCRS was established to simultaneously quantify the contents of polysaccharide, total lipids, protein and Chl-a in C. cryptica, with thirteen Raman bands were found to be the main marker bands for the diatom components. Moreover, Partial Least Square Regression (PLSR) models based on full spectrum can reliably predict these four cellular components, with Pearson correlation coefficient for these components reached 0.949, 0.904, 0.801 and 0.917, respectively. Finally, based on SCRS data of one isogenic sample, the pairwise correlation and dynamic transformation process of these components can be analyzed by Intra-ramanome Correlation Analysis (IRCA), and the results showed silicon starvation could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism. Conclusions First, method for the simultaneous quantification of the polysaccharide, total lipid, protein and pigment in single C. cryptica cell are established. Second, the instant interconversion of intracellular components was constructed through IRCA, which is based on data set of one isogenic population and more precision and timeliness. Finally, total results indicated that silicon deficiency could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.

IntroductionAttenuated total reflection (ATR)–Fourier transform infrared (FTIR) analysis is a rapid tool and represents a snapshot of all molecules present in a (plant) sample. Most alternative techniques for biochemical analyses of plant biomass require destructive sampling, complex and laborious sample pre-treatment, and precise and costly analysis. These analyses are often limited to soluble compounds instead of all compounds present. Such complicated procedures are not efficient for manipulative studies that involve repeated sampling and rapid nutrient changes over time, such as in agro-industrial cultivation studies.MethodsIn our study, the green seaweed species Ulva laetevirens (Chlorophyta) was cultivated under different nutritional regimes in onshore cultivation tanks. The regimes were nitrogen and phosphorus repletion, nitrogen depletion, phosphorus depletion, and light limitation. Samples were taken and tested according to common laborious analysis methods to determine the biochemical composition of polysaccharides, proteins, carbon, and nitrogen. These results were compared with the potential of ATR-FTIR spectroscopy combined with multivariate analysis to allow for prediction of biomass composition.ResultsStatistical analysis of the spectra showed that the samples were clustered according to the nutritional regime during the incubation of U. laetevirens. This made it possible to deduce which abiotic factors were replete or deplete during cultivation. Furthermore, partial least square regression analysis proved the most suitable method to predict carbohydrate concentration and nitrogen content present in the biomass.Discussion/conclusionOn the basis of these findings, it is concluded that ATR-FTIR spectroscopy is an efficient and rapid alternative tool for qualitative and quantitative determination of the biochemical composition of U. laetevirens that can be used in industrial cultivation setups.

Background: At present, the conventional methods for determining photosynthetic products of microalgae are usually based on a large number of cell mass to reach the measurement baseline, and the result can only reveal the average state at the population level, which is not feasible for large-scale and rapid screening of specific phenotypes from a large number of potential microalgae mutants. In recent years, single-cell Raman spectra (SCRS) has been proved to be able to rapidly and simultaneously quantify the biochemical components of microalgae. However, this method has not been reported to analyze the biochemical components of Cyclotella cryptica (C. cryptica). Thus, SCRS was first attempt to determine these four biochemical components in this diatom. Results: The method based on SCRS was established to simultaneously quantify the contents of polysaccharide, total lipids, protein and Chl-a in C. cryptica, with thirteen Raman bands were found to be the main marker bands for the diatom components. Moreover, Partial Least Square Regression (PLSR) models based on full spectrum can reliably predict these four cellular components, with Pearson correlation coefficient for these components reached 0.949, 0.904, 0.801 and 0.917, respectively. Finally, based on SCRS data of one isogenic sample, the pairwise correlation and dynamic transformation process of these components can be analyzed by Intra-ramanome Correlation Analysis (IRCA), and the results showed silicon starvation could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism. Conclusions: First, method for the simultaneous quantification of the polysaccharide, total lipid, protein and pigment in single C. cryptica cell are established. Second, the instant interconversion of intracellular components was constructed through IRCA, which is based on data set of one isogenic population and more precision and timeliness. Finally, total results indicated that silicon deficiency could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism. [ABSTRACT FROM AUTHOR]

Excessive total nitrogen (TN) content in topsoil is a major cause of eutrophication when nitrogen flows into water systems from soil losses. Therefore, TN content prediction is essential for establishing topsoil management systems and protecting aquatic ecosystems. Recently, hyperspectral imaging (HSI) has been used as a rapid, nondestructive technique for quantifying various soil properties. This study developed a machine and deep learning-based model using hyperspectral imaging to rapidly measure TN contents. A total of 139 topsoil samples were collected from the four major rivers in the Republic of Korea. Visible-to-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging data were acquired in the 400–1000 nm and 895–1720 nm ranges, respectively. Prediction models for predicting the TN content in the topsoil were developed using partial least square regression (PLSR) and one-dimensional convolutional neural networks (1D-CNNs). From the total number of pixels in each topsoil sample, 12.5, 25, and 50% of the pixels were randomly selected, and the data were augmented 10 times to improve the performance of the 1D-CNN model. The performances of the models were evaluated by estimating the coefficients of determination (R2) and root mean squared errors (RMSE). The Rp2 values of the optimal PLSR (with maximum normalization preprocessing) and 1D-CNN (with SNV preprocessing) models were 0.72 and 0.92, respectively. Therefore, HSI can be used to estimate TN content in topsoil and build a topsoil database to develop conservation strategies.

The accurate identification of microorganisms belonging to vaginal microflora is crucial for establishing which microorganisms are responsible for microbial shifting from beneficial symbiotic to pathogenic bacteria and understanding pathogenesis leading to vaginosis and vaginal infections. In this study, we involved the surface-enhanced Raman spectroscopy (SERS) technique to compile the spectral signatures of the most significant microorganisms being part of the natural vaginal microbiota and some vaginal pathogens. Obtained data will supply our still developing spectral SERS database of microorganisms. The SERS results were assisted by Partial Least Squares Regression (PLSR), which visually discloses some dependencies between spectral images and hence their biochemical compositions of the outer structure. In our work, we focused on the most common and typical of the reproductive system microorganisms (Lactobacillus spp. and Bifidobacterium spp.) and vaginal pathogens: bacteria (e.g., Gardnerella vaginalis, Prevotella bivia, Atopobium vaginae), fungi (e.g., Candida albicans, Candida glabrata), and protozoa (Trichomonas vaginalis). The obtained results proved that each microorganism has its unique spectral fingerprint that differentiates it from the rest. Moreover, the discrimination was obtained at a high level of explained information by subsequent factors, e.g., in the inter-species distinction of Candida spp. the first three factors explain 98% of the variance in block Y with 95% of data within the X matrix, while in differentiation between Lactobacillus spp. and Bifidobacterium spp. (natural flora) and pathogen (e.g., Candida glabrata) the information is explained at the level of 45% of the Y matrix with 94% of original data. PLSR gave us insight into discriminating variables based on which the marker bands representing specific compounds in the outer structure of microorganisms were found: for Lactobacillus spp. 1400 cm−1, for fungi 905 and 1209 cm−1, and for protozoa 805, 890, 1062, 1185, 1300, 1555, and 1610 cm−1. Then, they can be used as significant marker bands in the analysis of clinical subjects, e.g., vaginal swabs. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a group of metabolic disorder characterized by high blood sugar levels. Monitoring of blood glucose levels at regular intervals plays a crucial role in the management of diabetes. The non-invasive real-time monitoring of glucose using near-infrared (NIR), Raman, acoustic and bio-impedance techniques have an edge over available invasive techniques but suffers from low Signal to Noise ratio (S/N) and other interferences. In the present work, we have attempted to improve S/N for the efficient detection of feeble signals corresponding to the physiological glucose concentrations. Investigations were carried out in the NIR region particularly from 800-1400 nm for the identification of the unique absorption features of glucose using UV-Vis NIR spectrophotometer with different ranges of glucose concentrations including 5 g/dl- 45 g/dl, 1400 mg/dl -2500 mg/dl, 35 mg/dl-650 mg/dl. Savitzky Golay (SG) pre-processing filter was applied on the raw data for enhancing the S/N for better prediction of glucose concentrations. The absorption spectra of glucose revealed the presence of a peak at 960 nm. Therefore, considering the absorbance at 960 nm, provided an enhancement in the S/N ratio from 17 dB to 27 dB. Further, partial least square regression (PLSR), has been applied on SG filtered data for a better prediction of glucose concentration with a correlation coefficient (R²) value of 0.99 and root mean square error of prediction (RMSE) of 2.29 mg/dl. Further, based on the NIR spectral data, we have developed a measurement technique using two LED sources of 950 nm and 860 nm, and a wide detector (700 - 1100 nm) which converts obtained optical signal into voltage. It has been observed that by considering dual wavelength detection points the prediction of glucose concentration is improved. Furthermore, with increase in the test glucose concentrations, the voltage signal decreased corresponding to the 950 nm LED. This is attributed to reduced signal intensities reaching the photodiode as a result of the increase in glucose absorption. Incorporating dual wavelengths for PLSR reduced the RMSE from 8.98 mg/dl to 6.49 mg/dl and also improved the R² value from 0.97 to 0.99. [ABSTRACT FROM AUTHOR]

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person's intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy ( R 2 (p r e d) > 90 % ). [ABSTRACT FROM AUTHOR]

China is the largest coal consumer in the world. The massive exploitation and utilization of coal resources have resulted in serious problems of heavy metal pollution and environmental contamination, such as soil degradation, water pollution, crop damage, and even threatening human lives. Therefore, monitoring soil heavy metal pollution quickly and in real time is an urgent task at present. This research not only formulated a new preprocessing method enlightened by few-shot learning for soil hyperspectral data but also combined it with other soil-related auxiliary information to extract effective information from the soil hyperspectrum, at the end of which different regression methods were adopted to predict soil heavy metal contamination. This test used 168 actual soil samples from the Eastern Junggar coalfield in Xinjiang for verification. Since copper in the soil is a trace element and the corresponding spectral characteristics are affected by other impurities, improper use of hyperspectral preprocessing methods may introduce interference information or may delete useful information, which makes the model effect unsatisfied. To effectively address the above-mentioned problems, the preprocessing method of this experiment combined the second-order differential derivation, and the data enhancement (DA) method together with the addition of auxiliary information to allow more effective features to be entered into the model. Next, the attentive interpretable tabular learning (TabNet) model was improved in three different ways using the original TabNet model and three improved TabNet models to create regression models. One of the improved TabNet models had the best effect, with a list of the top 30 features according to the degree of importance. Meanwhile, the regression prediction of Cu content using four different convolutional neural networks (CNNs) revealed that the model with the residual block was the strongest and slightly outperformed the improved TabNet model, but lacked interpretation of the input data. Besides, this experiment also employed different preprocessing methods for regression prediction on various models and found that the traditional preprocessing methods performed best in traditional regression models [e.g., partial least square regression (PLSR)] and underperformed in deep learning models. The selected optimal model was compared with PLSR and CNN models. The results indicated that both the improved TabNet model and the improved CNN model had better performance using the new preprocessing approach proposed in this article, with improved TabNet yielding a coefficient of determination ($\text{R}^{2}$), root-mean-square error (RMSE), and the ratio of performance to interquartile range (RPIQ) of 0.94, 1.341, and 4.474, respectively. The improved CNN model had a coefficient of determination of 0.942, an RMSE of 1.324, and an interquartile range of 4.531 in the test dataset. [ABSTRACT FROM AUTHOR]

An electrochemical detection of inositol content using platinum (Pt)-based noble metal electrode is investigated. In this work, the electrochemical behavior of the platinum electrode has been studied and analyzed using a three-electrode system against a silver–silver chloride (Ag/AgCl) reference electrode and a steel counter electrode. Differential pulse voltammetry technique has been employed for this experimental study. A satisfactory linear range of operation was obtained from 50 to 400 μ M with LOD = 1 9. 2 8 μ M. Electrochemical responses for several inositol concentrations 50, 80, 100, 200, 300 and 400 μ M have also been analyzed using principal component analysis (PCA) with effective data clustering. A good class separability index (SI) was found to be 142.91. In addition, a prediction estimation of inositol contents using partial least square regression (PLSR) and principal component regression (PCR) algorithms were also evaluated and prediction accuracies of 93.69% and 93.71% were obtained, respectively. Moreover, the application of the Pt electrode over real orange juice sample extracts revealed satisfactory recovery rate of 96.18%. Thus, this technique of electrochemical system may be subjected for inositol detection in our daily-life food (especially juice, beverages) consumption. [ABSTRACT FROM AUTHOR]

This study evaluated the sensory characteristics of sauerkraut prepared by adding 0.5, 1.0, 1.5, 2.0, and 2.5% (w/w) sea salt to cabbage. The quantitative descriptive analysis (QDA) and acceptance test of sauerkraut were determined for each salt concentration, and the principal component analysis (PCA) and partial least square regression (PLSR) analysis were performed to confirm the correlation between each factor. Results of the QDA determined 14 descriptive terms; furthermore, brightness and yellowness of appearance and the sour, salty, and bitter flavors differed significantly according to the salt concentration. Results from the PCA explained 22.56% PC1 and 65.34% PC2 of the total variation obtained. Sauerkraut prepared using 0.5, 1.0, and 1.5% sea salt had high brightness, moistness, sour odor, green odor, sour flavor, carbonation, hardness, chewiness, and crispness, whereas sauerkraut prepared with 2.0 and 2.5% sea salt had high yellowness, glossiness, salty flavor, sweet flavor, and bitter flavor. Hierarchical cluster analysis classified the products into two clusters: sauerkraut of 0.5, 1.0, and 1.5%, and sauerkraut of 2.0 and 2.5%. Results of PLSR determined that sauerkraut of 1.0 and 1.5% were the closest to texture, taste, and overall acceptance. We, therefore, conclude that sauerkrauts prepared using 1.0 and 1.5% sea salt have excellent characteristics in appearance, taste, and texture. [ABSTRACT FROM AUTHOR]

Soil nitrogen density (SND), which is influenced by environmental factors operating at different spatial scales and intensities, is critical for agricultural production and soil quality. Although the spatiotemporal distribution of top-layer SND has been well explored, the scale effects of environmental factors on the temporal changes of SND (SNDT) are poorly studied, which might promote the predictive accuracy of SNDT. Thus, SNDT during a certain period was calculated to explore the multiscale effects of environmental factors on it. In the study, three sampling transects under the basins of warm-temperate, mid-temperate, and warm-temperate zones were established with 200 km long and 1 km intervals to explore the spatial variation of SNDT, examine the multiscale effect of environmental factors on it, construct the predicting models based on its scale-specific relations with environmental factors, and validate the models in each basin or in other climate-zone basins. The results indicated that the increment of SND during a certain period was the greatest in the mid-temperate basin, and the variation of SNDT was ranked as cool-temperate > mid-temperate > warm-temperate basins. Under different soil types, the spatial characteristics of SNDT were different in different climate-zone basins, but the average SNDT under cropland was the greatest in each basin. Considering the influencing factors (climatic, topographic, and vegetation factors), they had controls on SNDT operating at different spatial scales. In regard to the prediction of SNDT, the method of partial least square regression (PLSR) combined with a multiscale analysis was found to be more preferable for dependent SNDT prediction than the traditional method of stepwise multiple linear regression but could not be validated for the independent validation data in other basins. Thus, the spatial multiscale relations of SNDT with environmental factors could provide more information for each basin, and the integration of the extra information decomposed by wavelet transform into the method of PLSR could enhance the SNDT prediction for dependent datasets. These findings are of great significance for future studies in the spatial modeling of SND temporal dynamics under the influence of environmental changes.

Mid-infrared spectroscopy using Fourier transform infrared (FTIR) with attenuated total reflectance (ATR) correction was coupled with partial least square regression (PLSR) for the prediction of phenolic acids and flavonoids in fig (peel and pulp) identified with high-performance liquid chromatography-diode array detector (HPLC-DAD), with regards to their partitioning between peel and pulp. HPLC-DAD was used to quantify the phenolic compounds (PCs). The FTIR spectra were collected between 4,000 and 450 cm–1 and the data in the wavenumber range of 1.175–940 cm–1, where the deformations of O-H, C-O, C-H, and C=C corresponded to flavanol and phenols, were used for the establishment of PLSR models. Nine PLSR models were constructed for peel samples, while six were built for pulp extracts. The results showed a high-throughput accuracy of such an approach to predict the PCs in the powder samples. Significant differences were detected between the models built for the two fruit parts. Thus, for both peel and pulp extracts, the coefficient of determination (R2) ranged from 0.92 to 0.99 and between 0.85 and 0.95 for calibration and cross-validation, respectively, along with a root mean square error (RMSE) values in the range of 0.46–0.9 and 0.23–2.05, respectively. Residual predictive deviation (RPD) values were generally satisfactory, where cyanidin-3,5-diglucoside and cyanidin-3-O-rutinoside had the higher level (RPD > 2.5). Similar differences were observed based on the distribution revealed by partial least squares discriminant analysis (PLS-DA), which showed a remarkable overlapping in the distribution of the samples, which was intense in the pulp extracts. This study suggests the use of FTIR-ATR as a rapid and accurate method for PCs assessment in fresh fig. Scheme diagram showing the research methodology and analytical approaches. [ABSTRACT FROM AUTHOR]

Mid-infrared spectroscopy using Fourier transform infrared (FTIR) with attenuated total reflectance (ATR) correction was coupled with partial least square regression (PLSR) for the prediction of phenolic acids and flavonoids in fig (peel and pulp) identified with high-performance liquid chromatography-diode array detector (HPLC-DAD), with regards to their partitioning between peel and pulp. HPLC-DAD was used to quantify the phenolic compounds (PCs). The FTIR spectra were collected between 4,000 and 450 cm–1 and the data in the wavenumber range of 1.175–940 cm–1, where the deformations of O-H, C-O, C-H, and C=C corresponded to flavanol and phenols, were used for the establishment of PLSR models. Nine PLSR models were constructed for peel samples, while six were built for pulp extracts. The results showed a high-throughput accuracy of such an approach to predict the PCs in the powder samples. Significant differences were detected between the models built for the two fruit parts. Thus, for both peel and pulp extracts, the coefficient of determination (R2) ranged from 0.92 to 0.99 and between 0.85 and 0.95 for calibration and cross-validation, respectively, along with a root mean square error (RMSE) values in the range of 0.46–0.9 and 0.23–2.05, respectively. Residual predictive deviation (RPD) values were generally satisfactory, where cyanidin-3,5-diglucoside and cyanidin-3-O-rutinoside had the higher level (RPD > 2.5). Similar differences were observed based on the distribution revealed by partial least squares discriminant analysis (PLS-DA), which showed a remarkable overlapping in the distribution of the samples, which was intense in the pulp extracts. This study suggests the use of FTIR-ATR as a rapid and accurate method for PCs assessment in fresh fig.

Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.

Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R 2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring. [ABSTRACT FROM AUTHOR]

Biofilm‐related infections occur quite frequently in hospital settings and require rapid diagnostic identification as they are recalcitrant to antibiotic therapy and make special treatment necessary. One of the standard microbiological in vitro tests is the crystal violet test. It indirectly determines the amount of biofilm by measuring the optical density (OD) of the crystal violet‐stained biofilm matrix and cells. However, this test is quite time‐consuming, as it requires bacterial cultivation up to several days. In this study, we correlate fast Raman spectroscopic read‐out of clinical Staphylococcus aureus isolates from 47 patients with different disease background with their biofilm‐forming characteristics. Included were low (OD < 10), medium (OD ≥ 10 and ≤20), and high (OD > 20) biofilm performers as determined by the crystal violet test. Raman spectroscopic analysis of the bacteria revealed most spectral differences between high and low biofilm performers in the fingerprint region between 750 and 1150 cm−1. Using partial least square regression (PLSR) analysis on the Raman spectra involving the three categories of biofilm formation, it was possible to obtain a slight linear correlation of the Raman spectra with the biofilm OD values. The PLSR loading coefficient highlighted spectral differences between high and low biofilm performers for Raman bands that represent nucleic acids, carbohydrates, and proteins. Our results point to a possible application of Raman spectroscopy as a fast prediction tool for biofilm formation of bacterial strains directly after isolation from the infected patient. This could help clinicians make timely and adapted therapeutic decision in future. [ABSTRACT FROM AUTHOR]

To monitor and predict the Groundwater levels in Sharifabad watershed, Central province, Iran three models of Partial Least Square Regression (PLSR), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used. In all models, 70% of the data was used for training, while 30% of data were employed for testing and validation. Monthly rainfall, topographic wetness index (TWI index), the distance from the river, Geographic location was the inputs and the level of groundwater was the output of each method. It is observed that ANN has the highest efficiency, which agrees with other findings. The results of ANN have been used in preparation of groundwater distribution map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Using ANN, it is predicted that 100% of the area was severely degraded for 2025. In addition to the target variable, latitude and longitude play important roles in ordinary Krigging and decreased the total error of two combined models. [ABSTRACT FROM AUTHOR]

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).

Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for measuring rice's protein content, demands considerable manpower, time, and costs, including preprocessing such as removing the rice husk. Therefore, of the technology is needed to rapidly and nondestructively measure the protein content of paddy rice during harvest and storage stages. In this study, the nondestructive technique for predicting the protein content of rice with husks (paddy rice) was developed using near-infrared spectroscopy and deep learning techniques. The protein content prediction model based on partial least square regression, support vector regression, and deep neural network (DNN) were developed using the near-infrared spectrum in the range of 950 to 2200 nm. 1800 spectra of the paddy rice and 1200 spectra from the brown rice were obtained, and these were used for model development and performance evaluation of the developed model. Various spectral preprocessing techniques was applied. The DNN model showed the best results among three types of rice protein content prediction models. The optimal DNN model for paddy rice was the model with first-order derivative preprocessing and the accuracy was a coefficient of determination for prediction, R p 2  = 0.972 and root mean squared error for prediction, RMSEP = 0.048%. The optimal DNN model for brown rice was the model applied first-order derivative preprocessing with R p 2  = 0.987 and RMSEP = 0.033%. These results demonstrate the commercial feasibility of using near-infrared spectroscopy for the non-destructive prediction of protein content in both husked rice seeds and paddy rice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang, Kim, Lee, Kim, Sang, Yang and Mo.)

For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops. [ABSTRACT FROM AUTHOR]

The potential of Raman spectroscopy has been utilized for the diagnosis of hepatitis B virus (HBV) infection in blood plasma. Raman spectra of 24 diseased and 10 healthy samples were used to develop distinct types of support vector machine (SVM) models, including linear, quadratic, and radial basis function (RBF) using multivariate method of principal component analysis (PCA) to reduce the dimensions of the obtained datasets. To assess the diagnostic power of these algorithms, developed models were tested on independent dataset. RBF‐based PCA‐SVM model achieved the best performance and yielded accuracy of 98.82%, sensitivity of 98.89%, and specificity of 98.80%. The performance of the SVM models was compared with rerated chemometric method of partial least square regression (PLSR), which has been developed by using the same dataset. The PLSR model attained the diagnostic accuracy of 88%, sensitivity of 93%, and specificity of 78% for same dataset. Our developed model has established promising results compared with state‐of‐the‐art approaches. The results reveal the improved performance of the developed chemometric techniques and clinical prediction potential of HBV by PCA‐SVMs in conjunction with Raman spectroscopy. [ABSTRACT FROM AUTHOR]

This work proposes a novel method for determining the composition of mixtures of natural polyphenolic compounds: caffeic acid, gallic acid, ellagic acid, rosmarinic acid and quercitrin in plants. The method is based on the formation of colored spots by these compounds upon reaction with nano-oxides of Al2O3, ZnO, MgO, CeO2, TiO2 and MoO3 impregnated on filter paper and constituting a colorimetric sensor array (CSA). The image of the colored spots was analyzed and the intensity of the blue colour (BCI) component has shown maximum sensitivity in relation to phenolic compounds. The inverse of BCI was linearly correlated with the logarithm of the individual phenolic compound concentrations. Chemometric analysis by partial least squares regression (PLSR) of 1/BCI values for 24 synthetic mixtures of the 5 phenolic compounds measured with the colorimetric sensor array has demonstrated good correlation between the actual and the predicted concentration of quercitrin. For the other phenolic compounds, the colors measured with the colorimetric sensor array were greatly influenced by the concentrations of the other components in the mixture. The method was applied to the determination of quercitrin in medicinal teas and the results were compared to those obtained by HPLC. The discussion of the results emphasizes possible interferences in the tea samples. While further optimization of the colorimetric sensor array-based method appears necessary, tailored to the particular targeted application in real samples, the proposed method for polyphenol determination has advantages that include simplicity, low cost, and portability. [ABSTRACT FROM AUTHOR]

To study the aroma features of different varieties of honey, five honey samples from different botanical origins were characterized by sensory evaluation, gas chromatography-mass spectrometry and electronic nose analysis. The sensory evaluation results gave a good reflection of the honey’s different aroma characteristics. A total of 55 volatile compounds were identified by headspace solid-phase micro-extraction followed by gas chromatography-mass spectrometry, and among these compounds, 13 were found in all of the honey samples. A number of differences were observed in the composition of volatile components from the five types of honey. Twenty-two compounds were selected as typical odor-active compounds which co-varied well with the five sensory attributes by partial least squares regression. The correlation results between the sensory profiles and electronic nose data showed that the electronic nose could give comparable results in predicting the sensory attributes of honey. In conclusion, the combination of sensory evaluation, gas chromatography-mass spectrometry analysis of volatile compounds, and electronic nose data with partial least squares regression analysis could be applicable for the overall analysis of aroma features for honey.

The correct recognition of sweet orange (Citrus sinensis L. Osbeck) variety accessions at the nursery stage of growth is a challenge for the productive sector as they do not show any difference in phenotype traits. Furthermore, there is no DNA marker able to distinguish orange accessions within a variety due to their narrow genetic trace. As different combinations of canopy and rootstock affect the uptake of elements from soil, each accession features a typical elemental concentration in the leaves. Thus, the main aim of this work was to analyze two sets of ten different accessions of very close genetic characters of three varieties of fresh citrus leaves at the nursery stage of growth by measuring the differences in elemental concentration by laser-induced breakdown spectroscopy (LIBS). The accessions were discriminated by both principal component analysis (PCA) and a classifier based on the combination of classification via regression (CVR) and partial least square regression (PLSR) models, which used the elemental concentrations measured by LIBS as input data. A correct classification of 95.1% and 80.96% was achieved, respectively, for set 1 and set 2. These results showed that LIBS is a valuable technique to discriminate among citrus accessions, which can be applied in the productive sector as an excellent cost–benefit tool in citrus breeding programs.

Desi ghee, obtained by buffalo and cow milk, is highly expensive because it contains valuable vitamins and conjugated linoleic acid (CLA). Its high demand and cost result in to its adulteration with inferior banaspati ghee. In this study, Fluorescence spectroscopy along with multivariate analysis has been utilised for the detection and quantification of adulteration. Spectroscopic analysis showed that buffalo ghee contains more vitamins and CLA than cow, whereas cow ghee is enriched with beta-carotene. For multivariate analysis, principle component analysis (PCA) and partial least square regression (PLSR) have been applied on the spectral data for the determination of adulteration. PLSR model was authenticated by predicting 23 unknown samples including 3 commercial brands of desi ghee. The root mean square error in prediction (RMSEP) of unknown samples was found to be 1.7 which is a reasonable value for quantitative prediction. Due to non-destructive and requiring no sample pre-treatment, this method can effectively be employed as on line characterization tool for the food safety assurance. [ABSTRACT FROM AUTHOR]

The level of groundwater can also be used in monitoring desertification and land degradation. In this study, three models, namely: partial least square regression, artificial neural networks (ANN), and adaptive neuro-fuzzy inference system, were used to monitor and predict the level of groundwater and the land degradation index via the Iranian Model of Desertification Potential Assessment method. The groundwater data of 24 Piezometric wells from 2002 to 2016 were also collated to predict the groundwater level. In all models, 70% of the data were applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, topographic wetness index, distance of the river (m), latitude and longitude of Piezometers in the Universal Transverse Mercator coordinate system were the inputs, and the level of groundwater was the output of each method. The prediction performance of both training and testing sets is evaluated by R2 and MSE. Looking at statistical inferences, we found that ANN has the highest efficiency (R2 = 0.96, MSE = 0.71 m) which agree with other findings. We combined the results of ANN with ordinary kriging (OK) and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Again between 2002 and 2016, the land area with low degradation risk decreased from 38,030 ha (39% of the study area) to zero ha in 2016. In 2016, there was no moderate land degradation risk. Using ANN, we predicted that around 99% of the area (95,206 ha) was severely degraded in 2017 and according to groundwater level index, the land degradation increased by 100%. This implies that the area deserves urgent care and reclamation. We also used latitude and longitude of Piezometers as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in OK and decreased the total error of two combined models. [ABSTRACT FROM AUTHOR]

Total viable count (TVC) is often used as an important indicator for chicken freshness evaluation. In this study, 112 fresh chicken flesh samples were acquired after slaughtered and their hyperspectral images were collected in the LW-NIR (900-1700 nm) range. The full LW-NIR spectra (486 wavebands) within the images were extracted and applied to related to reference TVC values measured in different storage periods, using partial least squares regression (PLSR) algorithm, resulting in high correlation coefficients (R) and low root mean square errors (RMSE), for either raw spectra or pretreatment spectra. By using regression coefficients (RC) method, 20, 18, 17 and 20 optimal wavebands were respectively selected from raw spectra, baseline correction (BC) spectra, Savitzky-Golay convolution smoothing (SGCS) spectra and standard normal variate (SNV) spectra and applied for the optimization of original full waveband PLSR model. By comparison, RC-PLSR model based on the SGCS spectra showed a better performance in TVC prediction with RC of 0.98 and RMSEC of 0.35 log10 CFU/g in calibration set, and RP of 0.98 and RMSEP of 0.44 log10 CFU/g in prediction set. At last, by transferring the best RC-PLSR model, the dynamic TVC change during the storage was visualized by color maps to indicate the TVC spoilage degree. The overall study revealed that LW-NIR hyperspectral imaging combined with PLSR could be used to predict the freshness of chicken flesh. [ABSTRACT FROM AUTHOR]

Abstract Ammopiptanthus mongolicus , the only drought-resistant, leguminous, evergreen shrub in the desert region of China, is endangered due to climate change and its growth stages urgently need to be non-destructively detected. Although many spectral indexes have been proposed for characterizing vegetation, the relationships are often inconsistent, making it challenging to characterize the status of vegetation across all growth stages. This study investigated the Spectral Features of the endangered desert plant A. mongolicus at different growth stages, and extracted the identified Spectral Features for the establishment of detection and discrimination models using Partial Least Square Regression (PLSR) and Fisher Linear Discriminate Analysis (FLDA), respectively. The results showed spectral reflectance of A. mongolicus differed across different growth stages and it generally increased with the degree of senescence. Poor performance was found in the single factor model, with RMSE ranging from 20.34 to 27.39 or Overall Accuracy of 60% in the validation datasets. The multivariate PLSR model, based on Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Physiological Reflectance Index (PhRI) and Plant Senescence Reflectance Index (PSRI), turned out to be accurate in detecting the growth stages, with R 2 of 0.89 and RMSE of 12.46, and the performance of the multivariate FLDA model based on 14 Spectral Features was acceptable, with an Overall Accuracy of 89% in the validation datasets. This research provides useful insights for timely and non-destructively discriminating different growth stages by using multivariate PLSR and FLDA analysis. Graphical abstract Unlabelled Image Highlights • A. mongolicus is the only drought-resisting leguminous evergreen shrub in arid China. • It is the first reported field study on in-situ monitoring of the endangered plant. • Hyperspectral measurement was conducted over A. mongolicus at different growth stage. • PLSR and FLDA are applicable to timely and non-destructively detect plant status. • The results have potential implications on vegetation and desert management. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources.

The content of chlorophyll, an important substance for photosynthesis in plants, is an important index used to characterize the photosynthetic rate and nutrient grade of plants. The real-time rapid acquisition of crop chlorophyll content is of great significance for guiding fine management and differentiated fertilization in the field. This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy. This task was conducted to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment. First, a Savitzky–Golay filter and standard normal variable processing were applied to the spectral data to eliminate the influence of random noise and limit drift on spectral reflectance. Second, CWT was performed on the spectral reflection curve with 10 frequency scales to obtain the wavelet energy coefficient of the spectral data. The characteristic bands related to chlorophyll content in the spectral data and the wavelet energy coefficients were screened using the maximum correlation coefficient and the local correlation coefficient extrema, respectively. A partial least-square regression model was established. Results showed that the characteristic bands selected via local correlation coefficient extrema in a wavelet energy coefficient created a detection model with optimal accuracy. The determination coefficient (Rc2) of the calibration set was 0.7856, and the root-mean-square error (RMSE) of the calibration set (RMSEC) was 3.0408. The determination coefficient (Rv2) of the validation set is was 0.7364, and the RMSE of the validation set (RMSEV) was 3.3032. Continuous wavelet transform is a process of data dimension enhancement which can effectively extract the sensitive variables from spectral datasets and improve the detection accuracy of models.

The potential of near-infrared spectroscopy (NIRS) for the quality control of traditional Chinese medicine has been evaluated. Seven quantitative parameters, andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, and alcohol-soluble extract of Andrographis paniculata, were evaluated by NIRS. The reference values of andrographolides were determined by high-performance liquid chromatography, and the others were obtained using the standard methods of the 2015 Chinese Pharmacopoeia. The predicted values were determined by a quantitative model using NIRS based on partial least square regression. Different spectral preprocessing methods, spectral ranges, and optimum number of factors were selected to optimize the models. All models were estimated by the combination of various parameters, including the correlation coefficient of calibration for andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, alcohol-soluble extract (values of 0.980, 0.984, 0.989, 0.983, 0.987, 0.988, 0.979, respectively), root mean square error of calibration (values of 0.156, 0.038, 0.050, 0.029, 0.604, 0.431, 0.135, respectively), root mean square error of prediction (values of 0.169, 0.041, 0.050, 0.033, 0.280, 0.493, 0.140, respectively), root mean square error of cross-validation (values of 0.626, 0.114, 0.158, 0.046, 1.145, 0.774, 0.508, respectively), and ratio of standard deviation to standard error of prediction (values of 4.583, 4.690, 4.796, 4.899, 4.899, 4.690, 5.099, respectively). The results show that the calibration models by NIRS are reliable and can be applied for the quantification for seven parameters from A. paniculata for quality control in traditional Chinese medicine production and processing. [ABSTRACT FROM AUTHOR]

Highlights • A comparison of WorldView-2 and Sentinel-2 to map Andean wetlands was performed. • A one-class classifier and PLSR were used to map and estimate soil moisture. • The sensors performed equally well for mapping wetlands. • Sentinel-2 outperforms WorldView-2 in the prediction of soil moisture. • The approach could be used to enhance the Chilean wetland inventory. Abstract In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression. The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r2 values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes. [ABSTRACT FROM AUTHOR]

Soil salinity and sodicity can significantly reduce the value and the productivity of affected lands, posing degradation, and threats to sustainable development of natural resources on earth. This research attempted to map soil salinity/sodicity via disentangling the relationships between Landsat 8 Operational Land Imager (OLI) imagery and in-situ measurements (EC, pH) over the west Jilin of China. We established the retrieval models for soil salinity and sodicity using Partial Least Square Regression (PLSR). Spatial distribution of the soils that were subjected to hybridized salinity and sodicity (HSS) was obtained by overlay analysis using maps of soil salinity and sodicity in geographical information system (GIS) environment. We analyzed the severity and occurring sizes of soil salinity, sodicity, and HSS with regard to specified soil types and land cover. Results indicated that the models’ accuracy was improved by combining the reflectance bands and spectral indices that were mathematically transformed. Therefore, our results stipulated that the OLI imagery and PLSR method applied to mapping soil salinity and sodicity in the region. The mapping results revealed that the areas of soil salinity, sodicity, and HSS were 1.61 × 106 hm2, 1.46 × 106 hm2, and 1.36 × 106 hm2, respectively. Also, the occurring area of moderate and intensive sodicity was larger than that of salinity. This research may underpin efficiently mapping regional salinity/sodicity occurrences, understanding the linkages between spectral reflectance and ground measurements of soil salinity and sodicity, and provide tools for soil salinity monitoring and the sustainable utilization of land resources. [ABSTRACT FROM AUTHOR]

To study the aroma features of different varieties of honey, five honey samples from different botanical origins were characterized by sensory evaluation, gas chromatography-mass spectrometry and electronic nose analysis. The sensory evaluation results gave a good reflection of the honey's different aroma characteristics. A total of 55 volatile compounds were identified by headspace solid-phase micro-extraction followed by gas chromatography-mass spectrometry, and among these compounds, 13 were found in all of the honey samples. A number of differences were observed in the composition of volatile components from the five types of honey. Twenty-two compounds were selected as typical odor-active compounds which co-varied well with the five sensory attributes by partial least squares regression. The correlation results between the sensory profiles and electronic nose data showed that the electronic nose could give comparable results in predicting the sensory attributes of honey. In conclusion, the combination of sensory evaluation, gas chromatography-mass spectrometry analysis of volatile compounds, and electronic nose data with partial least squares regression analysis could be applicable for the overall analysis of aroma features for honey. [ABSTRACT FROM AUTHOR]

Chemical absorption using amines is a suitable method to separate CO 2 from CO 2 rich natural gas stream. An instantaneous monitoring of CO 2 concentration in amine solvent is essential for an efficient chemical absorption process. A spectroscopic technique such as Raman spectroscopy along with multivariate modeling is considered as a robust and fast analytical method. It has been applied to monitor CO 2 concentration in a chemical absorption process. However, these studies are limited to low CO 2 loadings (<0.5 mol CO2 /mol amine ) and cannot be extrapolated to high CO 2 loading conditions. The evaluation of Raman method at high CO 2 loading is essential for the application at high pressure gas streams. In the present study, Raman spectroscopy is non-invasively applied to monitor CO 2 concentration in aqueous amines (DEA, MDEA, and their blends) over a wide range of CO 2 loadings (0.04–1.3 mol CO2 /mol amine ). The partial least square regression (PLSR) calibration models are developed and validated accordingly. The prediction accuracy is reported using determination coefficient (R 2 ) and root mean square error (RMSE). The average validation R 2 V and RMSE V for all the studied systems are calculated as 0.94 and 0.064 mol CO2 /mol amine respectively. These values show that Raman spectroscopy with PLSR is a promising technique to monitor CO 2 concentration for a wide range of CO 2 loading. The improvement in CO 2 monitoring is expected to enhance the process efficiency of natural gas processing plants. [ABSTRACT FROM AUTHOR]

The aim of this research was to correlate the chemical and physical characteristics of bitumen samples with the Fourier Transform infrared (FTIR) spectroscopy. Based on the FTIR analyses of 32 bitumen samples of different refineries, viscosity and ageing states using the attenuated total reflection (ATR) technology with multiple reflections, the gained spectra were evaluated with the chemometrical approach including multivariate analysis methods. With the Linear Discriminant Analysis (LDA), the bitumen samples could be distinguished according to the refinery even if the grades and the ageing states of the samples varied. In addition, the Partial Least Square Regression (PLSR) enables the determination of linear combinations for the description of different chemical, conventional and rheological parameters including the asphaltene content c asp , the softening point T R & B , the needle penetration PEN , the complex shear modulus | G ∗ | and the phase angle δ . Thereby, the separated consideration of the refineries allows the prediction of the parameters within the permitted limits according to the respective standards. Only for the softening point, the deviation of the calculated values exceeds the permitted deviations. [ABSTRACT FROM AUTHOR]

Quick advancement of industrial robots along with its usage by the assembling industries for various applications is a basic assignment for the determination of robots. As an outcome, the choice procedure of the robot turns out to be particularly entangled for the potential users since they have a broad arrangement of parameters of the accessible robots. In this paper, Partial Least Square Regression (PLSR) and Principal Component Regression (PCR) algorithm are utilized for the selection of industrial robots. In this proposed technique, eleven different parameters are taken as direct inputs for selecting a robot as compared to those of the existing models, which are limited up to seven parameters. Basing upon the proposed algorithm, the rank of an industrial robot is estimated. Moreover, the best robot that has been selected should satisfy the benchmark genuinity for a targeted application. In addition to this, the robot selection algorithm is measured through Mean Square Error (MSE), and Root Mean Square Error (RMSE), R-squared error(RSE). [ABSTRACT FROM AUTHOR]