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AbstractPanax notoginseng (Burk.) F.H. Chen (P. notoginseng), a precious medicinal herb, provides both considerable values. There is confusion in the market regarding the geographical origins of P. notoginseng. It is of paramount importance to authenticate geographical origins of P. notoginseng, which will facilitate the rational utilization of resources and sustainable development. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investigate the influence of the tissue and data fusion strategy for the authentication of P. notoginseng origins. The PLS-DA model employing the main root spectra demonstrated great potential for authenticating P. notoginseng origin. The PLS-DA training and test sets based on feature-level fusion (VIP) spectral data were 100% accurate, indicating excellent potential in authenticating P. notoginseng. The data fusion strategy improved the authentication. The article presents a reliable and efficacious novel methodology for authenticating P. notoginseng origins and a scientific foundation for its quality control. [ABSTRACT FROM AUTHOR]

AbstractCalyx of Diospyros kaki Thunb. (DKC), a famous traditional Chinese medicine, has a remarkable therapeutic effect on hiccups but its quality control is insufficient. In the study, a method was established by ultra-high performance liquid chromatography – mass spectrometry (UHPLC-MS). 17 peaks were observed in the mass spectrum and their chemical structures were preliminarily identified, including 3 organic acids and 14 flavonoids. The reference UHPLC fingerprint was built based on 25 batches of DKC samples, containing 15 common peaks. 11 batches of DKC samples from Guangxi and Hebei were superior to other 14 batches, sharing high similarity from 0.966 to 0.988 with the reference fingerprint. The chemometric methods, including hierarchical clustering analysis (HCA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA), were used to distinguish the DKC samples. The recognition methods clearly distinguished the medicinal materials from different regions, indicating that the main chemical constituents leading to the differences were protocatechuic acid, 4-hydroxybenzoic acid, rutin (presumptive result based on MS data), isoquercitrin, kaempferol, and hyperoside. The established fingerprint method is simple, rapid, repeatable, and specific and provides a reference for the quality evaluation of DKC. [ABSTRACT FROM AUTHOR]

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Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition. [ABSTRACT FROM AUTHOR]

The pollution from waste plastic express packages (WPEPs), especially microplastic (MP) fragments, caused by the blowout development of the express delivery industry has attracted widespread attention. On account of the variety of additives, strong complexity, and high diversity of plastic express packages (PEPs), the multi-class classification of WPEPs is a typical large-class-number classification (LCNC). The traceability and identification of microplastic fragments from WPEPs is very challenging. An effective chemometric method for large-class-number classification would be very beneficial for the comprehensive treatment of WPEP pollution through the recycling and reuse of waste plastic express packages, including microplastic fragments and plastic debris. Rather than using the traditional one-against-one (OAO) and one-against-all (OAA) dichotomies, an exhaustive and parallel half-against-half (EPHAH) decomposition, which overcomes the defects of the OAO's classifier learning limitations and the OAA's data proportion imbalance, is proposed for feature selection. EPHAH analysis, combined with partial least squares discriminant analysis (PLS-DA) for large-class-number classification, was performed on 750 microplastic fragments of polyethylene WPEPs from 10 major courier companies using near-infrared (NIR) spectroscopy. After the removal of abnormal samples through robust principal component analysis (RPCA), the root mean square error of cross-validation (RMSECV) value for the model was reduced to 0.01, which was 21.5% lower than that including the abnormal samples. The best models of PLS-DA were obtained using SNV combined with SG-17 smoothing and 2D (SNV+SG-17+2D); the latent variables (LVs), the error rates of Monte Carlo cross-validation (ERMCCVs), and the final classification accuracies were 6.35, 0.155, and 88.67% for OAO-PLSDA; 5.37, 0.103, and 87.33% for OAA-PLSDA; and 3.12, 0.054, and 96.00% for EPHAH-PLSDA. The results showed that the EPHAH strategy can completely learn the complex LCNC decision boundaries for 10 classes, effectively break the tie problem, and greatly improve the voting resolution, thereby demonstrating significant superiority to both the OAO and OAA strategies in terms of classification accuracy. Meanwhile, PLS-DA further maximized the covariance and data interpretation abilities between the potential variables and categories of microplastic debris, thereby establishing an ideal performance identification model with a recognition rate of 96.00%. [ABSTRACT FROM AUTHOR]

The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio. [ABSTRACT FROM AUTHOR]

Objective: To investigate the differences in volatile odor substances of Cyclocarya paliurus tea from different origins. Methods: Taking Cyclocarya paliurus tea from Changde, Zhangjiajie, Shaoyang in Hunan, Xiushui in Jiangxi, Qiandongnan in Guizhou, and Enshi in Hubei as the research subjects, the gas chromatography-ion mobility spectrometry (GC-IMS) coupled with partial least squares discriminant analysis (PLS-DA) was used to investigate the characteristics of different volatiles and their similarities in green strychnine teas from different origins. Results: A total of 120 VOCs, including monomers of some substances and their polymers, were detected in Cyclocarya paliurus tea from different origins, which were 34 aldehydes, 21 olefins, 19 alcohols, 16 ketones, 10 esters, 8 carboxylic acids, 5 furans, 4 pyrazines, 2 ethers, and 1 benzene species, respectively. Among them, γ-pinene, styrene, 4-methyl-1-pentanol, and n-octanal were the main characteristic difference volatiles. Conclusion: There were some differences in the volatile organic compounds of Cyclocarya paliurus tea among different origins(P＜0.05). It is possible to distinguish between different origins of cymbopogon tea based on the characteristic volatile substances. GC-IMS technology can effectively realize the origin identification and quality control of GC tea.

The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5−year−old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400–2500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.

Objective: This study aimed to investigate the effects of granularity of paprika on the physical and chemical properties and volatile flavor compounds of chili oil. Methods: Chili oil samples（KLD2-KLD5）were prepared from mechanically crushed paprika with different granularity （35, 30, 26, 20 mesh）, and the content of capsaicinoids, chromatic aberration value, and peroxide value of oil samples were determined by high performance liquid chromatography（HPLC）, colorimeter and other methods. The types and contents of volatile flavor compounds were detected and analyzed by gas chromatography-ion mobility spectrometry （GC-IMS） combined with principal component analysis （PCA）, partial least squares discriminant analysis （PLS-DA）, and heat map cluster analysis. In addition, a comparative analysis was performed with the traditional hand-milled chili oil sample （KLD1）. Results: In the KLD2-KLD5 chili oil samples prepared with mechanically crushed paprika, the concentration of capsaicin, dihydrocapsaicin and capsaicinoids, scoville heat units （SHU） and pungency degree decreased with the increase of the granularity of paprika. The peroxide value increases with the increase of the granularity, and the brightness L* increased first and then decreased with the decrease of the granularity, and there was a significant difference（P＜0.05） had been observed. A total of 58 volatile organic compounds （VOCs） were identified by GC-IMS, mainly including: alcohols, aldehydes, ketones, carboxylic acids, esters, heterocyclics and thioethers, with 10, 18, 12, 4, 7, 5 and 2 types respectively. GC-IMS fingerprints combined with the relative percentage of VOCs showed that the types of VOCs in KLD2-KLD5 samples were the same, but the contents were different. The types and content of VOCs in KLD1 were quite different from those in KLD2-KLD5. Fourteen key differential markers of 5 chili oils were screened by PLS-DA. The results of principal component analysis, nearest neighbor analysis, and heat map clustering analysis of VOCs in five kinds of chili oil samples were consistent with the results of GC-IMS fingerprints. These samples could be accurately distinguished and the flavor of KLD1 was the most unique. Conclusion: The granularity of paprika had a significant impact on the dissolution rate of capsaicin and dihydrocapsaicin in chili oil, peroxide value, and brightness L* （P＜0.05）, but has no effect on the types of volatile flavor compounds in chili oil. However, the content of volatile flavor compounds in each sample had a certain difference.

In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)—the general mechanisms affecting plant processes—has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development. [ABSTRACT FROM AUTHOR]

A method for the rapid identification of off-flavoured raw pork was investigated in this study as the off-flavoured raw pork often found in slaughtering and quarantine and brought the economic loss to the pork industry. The electronic nose (e-nose) was used to analyse the volatile compounds of normal and off-flavoured pork from two cuts (plum and hind legs) and principal component analysis (PCA), linear discriminant analysis (LDA) and random forest (RF) were combined to identify and classify the pork samples. It was also verified by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The results showed that the PCA, LDA and RF models could effectively differentiate off-flavoured pork from normal pork by using e-nose detection. The test set of hind leg meat showed better classification accuracy than plum meat, which were 91% and 81% respectively. W1S, W5C, W3C, W1C and W2W were the key sensors in the e-nose detection. A total of 50 odour substances were detected by HS-GC-IMS, including 11 ketones, 10 aldehydes, 8 esters, 5 acids, 6 alcohols, 9 other substances (including sulphur and nitrogen containing substances) and 1 uncharacterised substance. Methyl acetate, 2-butanone, 2-hexanone, n-propanol, ethyl isovalerate, and 2-pentylfuran were identified as volatile markers to distinguish between normal and off-flavoured pork screened by using partial least squares discriminant analysis (PLS-DA). The results of the e-nose and HS-GC-IMS measurements were in good agreement, confirming that the e-nose technique could be used for identification and discrimination of off-flavoured raw pork, which would provide a technical reference for the rapid identification of off-flavoured raw pork.

Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

The pollution from waste plastic express packages (WPEPs), especially microplastic (MP) fragments, caused by the blowout development of the express delivery industry has attracted widespread attention. On account of the variety of additives, strong complexity, and high diversity of plastic express packages (PEPs), the multi-class classification of WPEPs is a typical large-class-number classification (LCNC). The traceability and identification of microplastic fragments from WPEPs is very challenging. An effective chemometric method for large-class-number classification would be very beneficial for the comprehensive treatment of WPEP pollution through the recycling and reuse of waste plastic express packages, including microplastic fragments and plastic debris. Rather than using the traditional one-against-one (OAO) and one-against-all (OAA) dichotomies, an exhaustive and parallel half-against-half (EPHAH) decomposition, which overcomes the defects of the OAO’s classifier learning limitations and the OAA’s data proportion imbalance, is proposed for feature selection. EPHAH analysis, combined with partial least squares discriminant analysis (PLS-DA) for large-class-number classification, was performed on 750 microplastic fragments of polyethylene WPEPs from 10 major courier companies using near-infrared (NIR) spectroscopy. After the removal of abnormal samples through robust principal component analysis (RPCA), the root mean square error of cross-validation (RMSECV) value for the model was reduced to 0.01, which was 21.5% lower than that including the abnormal samples. The best models of PLS-DA were obtained using SNV combined with SG-17 smoothing and 2D (SNV+SG-17+2D); the latent variables (LVs), the error rates of Monte Carlo cross-validation (ERMCCVs), and the final classification accuracies were 6.35, 0.155, and 88.67% for OAO-PLSDA; 5.37, 0.103, and 87.33% for OAA-PLSDA; and 3.12, 0.054, and 96.00% for EPHAH-PLSDA. The results showed that the EPHAH strategy can completely learn the complex LCNC decision boundaries for 10 classes, effectively break the tie problem, and greatly improve the voting resolution, thereby demonstrating significant superiority to both the OAO and OAA strategies in terms of classification accuracy. Meanwhile, PLS-DA further maximized the covariance and data interpretation abilities between the potential variables and categories of microplastic debris, thereby establishing an ideal performance identification model with a recognition rate of 96.00%.

Accurate detection of chilling injury in kiwifruit is challenging because the symptoms are mainly manifested in the interior. This work reports a method for detecting the chilling injury of 'Hongyang' kiwifruit to provide nondestructive discrimination. Kiwifruit samples with varying levels of chilling injury were analyzed by a hyperspectral structuredillumination reflectance imaging (SIRI) system. After demodulation, direct current (DC) and alternating current (AC) images with spatial frequencies of 30, 60, and 120m-1 were obtained and labeled as F30, F60, and F120. Predictive models were developed to optimize the preprocessing and modeling methods. Prediction models established the results of DC and AC with different spatial frequencies and were compared. The autoscale-support vector machine (SVM) models were optimal for AC at different spatial frequencies, and the multiplicative scatter correction (MSC)-SVM model was optimal for DC. The combined features of F30, F60, and F120, as well as the spectral features of DC, had better accuracy for classifying chilling injury. The optimal model of hyperspectral SIRI system for detecting chilling injury was the F30 based on combined features, with calibration accuracy of 98.1% and prediction accuracy of 94.2%. This study has shown that structured illumination had higher accuracy than uniform illumination in predicting chilling injury. Further, this approach allows the identification of kiwifruit with chilling injury using a hyperspectral structured-illumination reflectance imaging system. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage. [ABSTRACT FROM AUTHOR]

In order to explore the composition of main volatile compounds and the difference of aroma compounds in Sauce-flavor Baijiu with different sweet flavor characteristics, sensory evaluation method was used to select Sauce-flavor Baijiu samples, the volatile components were analyzed by headspace solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS), and partial least squares discriminant analysis (PLS-DA) was used to analyze different Baijiu samples and their flavor differences substances. The results showed that the samples were divided into three groups, and the intensity values of sweet-flavor in each group were 4.0~5.0, 3.0~4.0 and 0.0~3.0, respectively, and a total of 68 volatile flavor compounds were characterized, including 27 esters, 12 alcohols, 10 aldehydes and ketones, 3 acids, 10 aromatics and 6 terpenes, among them, esters, aromatics and alcohols with sweet and fruity aromas were the three most abundant types of compounds in different Baijiu samples, and the content was the highest in Baijiu samples with a sweet flavor intensity greater than 3.0, indicating that these three types of substances had an important impact on the flavor characteristics of sweet aroma. There were 25 potential difference compounds affecting the three groups of Baijiu samples, and the related sweet flavor markers were mainly 3-methyl butanol, ethyl octanoate, isobutyl lactate and ethyl phenylacetate, indicating that these substances were the important aroma compounds that cause differences between different sauce-flavor Baijiu samples with different sweet flavor characteristics.

Introduction: Cannabis sativa L. inflorescences are rich in secondary metabolites, particularly cannabinoids. The most common techniques for elucidating cannabinoid composition are expensive technologies, such as high‐pressure liquid chromatography (HPLC). Objectives: We aimed to develop and evaluate the performance of a novel fluorescence spectroscopy‐based method coupled with N‐way partial least squares regression (N‐PLS‐R) and partial least squares discriminant analysis (PLS‐DA) models to replace the expensive chromatographic methods for preharvest cannabinoid quantification. Methodology: Fresh medicinal cannabis inflorescences were collected and ethanol extracts were prepared. Their excitation–emission spectra were measured using fluorescence spectroscopy and their cannabinoid contents were determined by HPLC‐PDA. Subsequently, N‐PLS‐R and PLS‐DA models were applied to the excitation–emission matrices (EEMs) for cannabinoid concentration prediction and cultivar classification, respectively. Results: The N‐PLS‐R model was based on a set of EEMs (n = 82) and provided good to excellent quantification of (−)‐Δ9‐trans‐tetrahydrocannabinolic acid, cannabidiolic acid, cannabigerolic acid, cannabichromenic acid, and (−)‐Δ9‐trans‐tetrahydrocannabinol (R2CV and R2pred > 0.75; RPD > 2.3 and RPIQ > 3.5; RMSECV/RMSEC ratio < 1.4). The PLS‐DA model enabled a clear distinction between the four major classes studied (sensitivity, specificity, and accuracy of the prediction sets were all ≥0.9). Conclusions: The fluorescence spectral region (excitation 220–400 nm, emission 280–550 nm) harbors sufficient information for accurate prediction of cannabinoid contents and accurate classification using a relatively small data set. Common techniques for elucidating cannabinoid composition are expensive chromatographic technologies. We aimed to develop novel fluorescence spectroscopy‐based method coupled with N‐way partial least squares regression (N‐PLS‐R) and discriminant analysis (PLS‐DA) models to replace chromatographic methods for cannabinoid quantification. Fresh cannabis inflorescences were collected and analyzed using fluorescence spectroscopy and their cannabinoid contents determined by HPLC for correlation purposes. The N‐PLS‐R model provided good quantification of major cannabinoids, and the PLS‐DA model enabled clear distinction between four major cannabis classes. [ABSTRACT FROM AUTHOR]

In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)—the general mechanisms affecting plant processes—has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development.

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L. [ABSTRACT FROM AUTHOR]

In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype. [ABSTRACT FROM AUTHOR]

Introduction: Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming., Objectives: This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences., Materials and Methods: Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models., Results: The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence., Conclusions: These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage., (© 2024 John Wiley & Sons Ltd.)

The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.

Rice's geographic origin and variety play a vital role in commercial rice trade and consumption. However, a method for rapidly discriminating the geographical origins of rice from a different region is still lacking. Therefore, the current study developed a volatile organic compound (VOC) based geographical discrimination method using headspace gas chromatography-mass spectrometry (HS-GC-MS) to discriminate rice samples from Heilongjiang, Jilin, and Liaoning provinces. The rice VOCs in Heilongjiang, Liaoning, and Jilin were analyzed by agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the optimum parameters for headspace solid phase microextraction (HS-SPME) involved the extraction of 3.0 g of rice at 80 °C within 40 min. A total of 35 VOCs were identified from 30 rice varieties from Northeast China. The PLS-DA model exhibited good discrimination (R2 = 0.992, Q2 = 0.983, and Accuracy = 1.0) for rice samples from Heilongjiang, Liaoning, and Jilin. Moreover, K-nearest neighbors showed good specificity (100%) and accuracy (100%) in identifying the origin of samples. In conclusion, the present study established VOC fingerprinting as a highly efficient approach to identifying rice's geographical origin. Our findings highlight the ability to discriminate rice from Heilongjiang, Liaoning, and Jilin provinces rapidly. [ABSTRACT FROM AUTHOR]

This study reports the utilization of two-step headspace (HS) to profile the volatile compounds of flue-cured tobacco. The relationship between the volatile components and geographic origin was investigated using multivariate data analysis. To avoid the impact of adsorption caused by tobacco on the HS extraction, the tobacco was soaked by saturated NaCl solutions prior to the HS sampling. However, the introduction of water vapor may affect the subsequent gas chromatography–mass spectrometry (GC–MS) analysis. Thus, two-step HS, which is based on the coupling of the programmed temperature vaporizer inlet and HS autosampler, was employed to remove the water vapor while simultaneously preconcentrating the volatile compounds. The repeatability and stability were evaluated, and 80 compounds were identified from 25 flue-cured tobacco samples. Both principal component analysis (PCA) and hierarchical clustering analysis (HCA) revealed that the volatile components of flue-cured tobacco were closely related to the geographic origin. Furthermore, the characteristic volatiles of the flue-cured tobacco from different regions were illustrated using partial least squares discriminant analysis (PLS-DA). The presented work provided a simple and reliable way to profile the volatile components and characterize geographic origin of tobacco and tobacco products. [ABSTRACT FROM AUTHOR]

[Display omitted] • IoT framework is established for discrimination of pigeon eggs by sensor detection. • NIR spectroscopy and its chemometric methods are studied for cloud computing. • A modified RWNN architecture is designed for intelligent model optimization. • Adaptive learning strategy is proposed to refine the network and its hyperparameters. In livestock industry, the female and male pigeons have different follow-up functions. The discrimination of female and male pigeons is an intensive concern for breeding tasks. In daily cultivation, the livestock staffs cannot distinguish the pigeon sex until the child pigeon is born. This lag in judgment seriously affects the freshness of pigeon eggs and timely sales plans. To solve this problem, we construct an internet of things (IoT) framework for modeling to discriminate a batch of pigeon eggs based on the instant data detection by visible and near-infrared (Vis-NIR) spectroscopy technology. In practice, the spectral detection data is monitored by multi-locational Vis-NIR sensors and immediately delivered to the cloud unit of the IoT framework. A random weight neural network (RWNN) architecture is designed as the intelligent computing module for model training and optimization, so that the cloud unit is able to deal with the constant inflow of Vis-NIR big data. An adaptive learning strategy is also designed to tune the network linkage weights as well as relevant hyperparameters. Partial least squares discriminant analysis is embedded in the Softmax unit for model discrimination, to optimize data processing with spectral properties. Experimental results proves that the adaptive RWNN architecture is able to observe high prediction accuracy when modeling on the early 5th-, 6th-, 7th- and 8th- hatching days for the distinguishment of the female and male pigeon eggs. Thus, the IoT-based Vis-NIR technology is prospectively expected to process the online big data in support with the adaptive RWNN modeling architecture. [ABSTRACT FROM AUTHOR]

Featured Application: QSAR model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions. The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection, was used to select the most relevant subsets of predictors. This led to a slight improvement in the predictive accuracies, and it has indicated that the most relevant descriptors for the discrimination of the investigated compounds into low- and high-permeable were associated with the 2D and 3D structures. [ABSTRACT FROM AUTHOR]

The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5-year-old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400-2500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L.

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Volatiles in 20 different varieties of L. edodes were fingerprinted. • A total of 56 types of volatile flavor compounds were identified using GC-IMS. • Twenty-eight compounds were selected for main characteristic aroma of L. edodes. • GC-IMS could effectively distinguish different varieties of L. edodes. The fingerprint database of volatile components in 20 varieties of Lentinula edodes were built using the gas chromatography-ion mobility spectroscopy (GC-IMS). Based on fingerprint and partial least squares discriminant analysis (PLS-DA), the differences in volatile components among varied L. edodes were further analyzed by cluster heat maps. A total of 57 volatile components were identified from all the mushroom samples, including 17 esters, 13 alcohols, 6 ketones, 6 aldehydes, 5 sulfur compounds, 3 acids, 1 alkane, and 6 other compounds. The varieties of ZP51, YS37, YS8, ZP46, XG172 showed the highest contents of esters (19 %∼41 %), alcohols (6 %∼17 %), ketones (4 %∼12 %), aldehydes (21 %∼48 %), sulfur compounds (7 %∼13 %), acids (2 %∼6 %), respectively. Twenty-eight key volatile flavor compounds were selected as the main characteristic aroma of L. edodes. GC-IMS could effectively distinguish different varieties of L. edodes , and the differences in the volatile components were visualized with fingerprint and cluster heat maps. These results provide a method to rapid evaluate the flavor profiles of L. edodes. [ABSTRACT FROM AUTHOR]

In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype.

Rice’s geographic origin and variety play a vital role in commercial rice trade and consumption. However, a method for rapidly discriminating the geographical origins of rice from a different region is still lacking. Therefore, the current study developed a volatile organic compound (VOC) based geographical discrimination method using headspace gas chromatography-mass spectrometry (HS-GC-MS) to discriminate rice samples from Heilongjiang, Jilin, and Liaoning provinces. The rice VOCs in Heilongjiang, Liaoning, and Jilin were analyzed by agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the optimum parameters for headspace solid phase microextraction (HS-SPME) involved the extraction of 3.0 g of rice at 80 °C within 40 min. A total of 35 VOCs were identified from 30 rice varieties from Northeast China. The PLS-DA model exhibited good discrimination (R2 = 0.992, Q2 = 0.983, and Accuracy = 1.0) for rice samples from Heilongjiang, Liaoning, and Jilin. Moreover, K-nearest neighbors showed good specificity (100%) and accuracy (100%) in identifying the origin of samples. In conclusion, the present study established VOC fingerprinting as a highly efficient approach to identifying rice’s geographical origin. Our findings highlight the ability to discriminate rice from Heilongjiang, Liaoning, and Jilin provinces rapidly.

Successful implementation of continuous manufacturing processes requires robust methods to assess and control product quality in a real-time mode. In this study, the residence time distribution of a continuous powder mixing process was investigated via pulse tracer experiments using near infrared spectroscopy for tracer detection in an in-line mode. The residence time distribution was modeled by applying the continuous stirred tank reactor in series model for achieving the tracer (paracetamol) concentration profiles. Partial least squares discriminant analysis and principal component analysis of the near infrared spectroscopy data were applied to investigate both supervised and unsupervised chemometric modeling approaches. Additionally, the mean residence time for three powder systems was measured with different process settings. It was found that a significant change in the mean residence time occurred when comparing powder systems with different flowability and mixing process settings. This study also confirmed that the partial least squares discriminant analysis applied as a supervised chemometric model enabled an efficient and fast estimate of the mean residence time based on pulse tracer experiments. [ABSTRACT FROM AUTHOR]

The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection, was used to select the most relevant subsets of predictors. This led to a slight improvement in the predictive accuracies, and it has indicated that the most relevant descriptors for the discrimination of the investigated compounds into low- and high-permeable were associated with the 2D and 3D structures.

Abstract MALDI time-of-flight mass spectrometry (MALDI-TOF MS) has become a widely used tool for the classification of biological samples. The complex chemical composition of pollen grains leads to highly specific, fingerprint-like mass spectra, with respect to the pollen species. Beyond the species-specific composition, the variances in pollen chemistry can be hierarchically structured, including the level of different populations, of environmental conditions or different genotypes. We demonstrate here the sensitivity of MALDI-TOF MS regarding the adaption of the chemical composition of three Poaceae (grass) pollen for different populations of parent plants by analyzing the mass spectra with partial least squares discriminant analysis (PLS-DA) and principal component analysis (PCA). Thereby, variances in species, population and specific growth conditions of the plants were observed simultaneously. In particular, the chemical pattern revealed by the MALDI spectra enabled discrimination of the different populations of one species. Specifically, the role of environmental changes and their effect on the pollen chemistry of three different grass species is discussed. Analysis of the group formation within the respective populations showed a varying influence of plant genotype on the classification, depending on the species, and permits conclusions regarding the respective rigidity or plasticity towards environmental changes.

BACKGROUND Heat treatment is the most common practice for the microbiological safety of milk; hence, determination of the heat treatment of milk is essential. Also, mislabeling or adulteration of expensive milk samples, like ewe or goat milk, with cow's milk is a growing problem in the dairy market. Thus, the determination of the authenticity of milk samples has crucial importance for both producers and consumers. The aim of this study was to discriminate milk samples using Raman spectroscopy with partial least squares discriminant analysis (PLS‐DA), first with regard to whether the milk was heat‐treated or not, and second with regard to species (cow, goat, ewe, mixture (adulterated)) in both raw and pasteurized milk. RESULTS: First, discrimination of milk samples as raw or pasteurized was achieved using PLS‐DA. Both in calibration and prediction models, high sensitivity and specificity values were obtained for raw and pasteurized milk samples. Second, the proposed method also discriminated milk samples according to their species (cow, goat, ewe, and mixture) for both raw and pasteurized milk. In both calibration and prediction models, the sensitivity and specificity values were above 0.857 and 0.897 respectively. Also, the accuracy values were above 0.915. The results obtained denote satisfactory accurate classification of the samples. CONCLUSION: The results suggest that Raman spectroscopy coupled with PLS‐DA can be successfully used to discriminate milk samples according to heat treatment (raw/pasteurized) and their species within 20 s per sample. It was seen that Raman spectra provide valuable information to be used especially for discrimination of milk samples according to their origin. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

A Zebra Chip (ZC) disease detection system was developed based on hyperspectral imaging (HSI) to minimise economic losses in the New Zealand potato chip industry. Current detection methods for other than heavily diseased tubers require peeling or cutting of potato tubers. A rapid and non-destructive grading method would be ideal to remove ZC diseased potatoes at line before processing. The spectral signatures from a large population (n = 3352) of commercially sourced potatoes were collected using HSI in the spectral range of 550 nm–1700 nm. Spectral signatures of each potato (i.e. 1767 ZC infected and 1585 healthy potatoes) were extracted by segmentation and morphological operations. A calibration dataset (80% of the total population was randomly selected), with and without pre-processing, was used for modelling using the partial least squares discriminant analysis (PLS-DA). The model performance shows 92% accuracy for ZC potato identification on validation data (20% of total population). Waveband optimisation by variable importance in projection (VIP) method revealed 34 wavebands sensitive to ZC diseased potatoes. This optimum set of wavebands allowed ZC identification with 89% accuracy. The experiments demonstrate the potential of HSI for identification of ZC infected potatoes in whole tuber before processing. Efficient removal of diseased tubers would reduce processing losses and provide a potential opportunity to access export markets for intact tubers. • Hyperspectral imaging (HSI) was used to identify Zebra Chip (ZC) infected potatoes. • PLS-DA models were developed to detect ZC infected potatoes gives 92% accuracy. • Variable important scores were resulted in 34 important wavebands for ZC potatoes. • Potential of HSI for non-destructive ZC diseased potatoes detection was proven. [ABSTRACT FROM AUTHOR]

In this work, the two-step headspace (HS) technique was applied to determine the actual volatile compositions of tea infusions. In order to eliminate the influence of water vapor to the gas chromatography-mass spectrometry (GC-MS) analysis and enable the capability to determine the volatile compounds in water base samples, a programed temperature vaporizer (PTV) was employed for the additional pre-concentration of thee analytes. While using the reported method, the volatile compounds were retained by the absorbent packed in the PTV, while the water vapor was directly removed through the purge valve. Then the retained volatile compounds were splitlessly injected into the GC column, which means that very low detection limits were achieved with the newly developed method. The obtained results demonstrated that the reported protocol possessed excellent repeatability and stability for volatile compound determination, and a total of 94 volatile compounds were identified from 28 teas. Multivariate data analysis was performed to obtain insights into the relationship between volatile compounds composition of the teas and their types. The volatiles composition of tea was shown to be closely related to the processing techniques adopted. Furthermore, the characteristic volatile compounds of each tea type were also illustrated. All of the results demonstrated that the presented method is a reliable approach to analyze and discriminate various beverages. [ABSTRACT FROM AUTHOR]

In order to investigate the flavor changes of beef sao zi industrial semi-finished products in different stir-frying stages. Sensory evaluation and instrumental analysis of tenderness were applied to optimize the processing parameters. For the overall odor perception, electronic nose (e-nose) can be used to distinguish the different odors of raw materials and processed samples. The volatile compounds were identified by headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS). A total of 144 volatile components were detected by HS-SPME-GC-MS, 52 of which were screened by t-test (P < 0.01).In the whole process, aldehydes were the major volatile flavor compounds in beef sao zi, which was consistent with the results of electronic nose in the stir-fry fat (SFF) stage. According to the results of partial least squares discriminant analysis (PLS-DA), the odor formation in stir-fried beef sao zi was divided into four steps including raw, stir-fry to remove water(SFMRW), stir-fry fat (SFF) and mixed stir-fry (MSF). The results showed that the method could effectively distinguish the different processing stages of beef sao zi. There were 27 volatile compounds in raw and SFMRW, 103 volatile compounds in SFF, and 14 volatile compounds in MSF. Among these stages, SFF stage plays important role in the flavor change of beef sao zi. Even in PLS-DA evaluation of scatter plot, the raw and SFMRW were in the fourth quadrant, but their samples could be clearly separated. In the fourth quadrant, there were 27 kinds of volatile compounds, among which 5 kinds of volatile compounds were unique to raw, 5 kinds were specific for SFMRW, and 17 kinds were common for raw and SFMRW. There were 103 volatile compounds in the second and third quadrants of PLS-DA evaluation of scatter plot, of which 25 were specific to SFF, and the remaining 78 were common in three processing stages. There were 14 volatile compounds in the first quadrant of the PLS-DA evaluation of scatter plot, which were shared with e MSF, raw, SFMRW and SFF. The amount of volatile compounds in the MSF stage was less than that in the SFMRW stage. The results showed that volatile compounds in the MSF stage was less than that in the SFMRW stage, but the 14 volatile compounds can be used to distinguish MSF from other processing stages. MSF stage was the final stage of flavor formation of stir-frying beef sao zi. The content of volatile flavor compounds in this stage was lower than that in SFF stage. On the one hand, after the end of SFF, most of the butter in fat tissue has been already spilled, and the rate of autoxidation and thermal oxidation cracking of fat were weakened. On the other hand, adding 70% sfmrw sample to 30% SFF sample can reduce MSF temperature to 126 ℃.Compared with the heat treatment temperature (145 ℃) in the SFF stage, the reaction rates were weakened. At the same time, the addition of 70% of SFMRW also diluted the volatile compounds generating in the SFF stage. [ABSTRACT FROM AUTHOR]

For reasons of productivity and throughput maximization, semiconductor equipment suppliers provide multiple-chamber machines to allow the split of production runs over parallel chambers. These latter are expected to perform identically and fabricate similar product quality, which is not usually the case given the low margin of error allowed in the complex manufacturing environment. The difficulty lies in achieving desired yields and controlling the process variability to accurately match the performance of those parallel chambers at all production steps. In this paper, a methodology to detect the mismatching chambers and to identify the root causes integrating all the sources of production data, such as the sensor data and product measurements, is proposed. The Partial Least Squares Discriminant Analysis is employed to separate clusters with high probability density based on classified samples, while providing key variables that most separate the known classes. The supervised classification method, applied to the summarized Fault Detection and Classification data, is followed by the analysis of temporal variables. The proposed approach will be validated with the real practices in an IC fabrication company. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract A comparison among widely used multivariate latent variable-based techniques for supervised process fault diagnosis was carried out. In order to assess their overall performance several diagnosis criteria were proposed ( C 1 : most suspected fault assignment; C 2 : threshold-based fault assignment). Additionally, it was evaluated i) how the size of the training set used to build the latent variable models affected the diagnosis ability of the methods under study, ii) how they behaved under new types of failures not included in the original list of fault candidates and iii) which of them were more suitable for either early or late diagnosis. To accomplish all these objectives, the approaches were tested in different scenarios. Two datasets were analysed: the first was generated by a Simulink-based model of a binary distillation column, while the second relates to a pasteurisation process performed in a laboratory-scale plant. Highlights • A comparison among four widely used multivariate latent variable-based fault diagnosis methods was carried out. • The approaches were tested in a simulated and a real case-study. • The effect of the training set size on the performance of the various techniques was evaluated. • Their behaviour under unexpected types of failures was assessed. • Their potential for early and late fault diagnosis was explored. [ABSTRACT FROM AUTHOR]

Abstract DRIFT spectra were used for classification of ZSM-5 catalysts according to their mesopore volumes. The spectra were pretreated by Savitzky-Golay smoothing and standard normal variate (SNV) algorithms prior to outlier detection by Hotelling T2 statistic technique. Supervised classification was applied to the spectra using partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogies (SIMCA) algorithms. The samples were classified into three classes related to their mesopore volumes by the proposed method and the results were in accordance with N 2 physisorption textural analysis using Brunauer–Emmett–Teller (BET) model. The confusion matrix and classification efficiency parameters including sensitivity, specificity, accuracy and precision were calculated. Classification accuracy of 96% and error rate of 2% was obtained using PLS-DA algorithm while SIMCA algorithm by providing 100% classification accuracy and zero error rate proved better performance in classification of ZSM-5 catalysts. Graphical Abstract DRIFT spectra were used for rapid and simultaneous classification of ZSM-5 catalysts according to their mesoporosity by applying PLS-DA and SIMCA chemometric algorithms. Unlabelled Image Highlights • DRIFT spectra were successfully used for classification of ZSM-5 catalysts according to their mesoporosity. • The results of the proposed methods were in accordance with N 2 physisorption analysis as the reference method. • SIMCA classification model provided for zero misclassification and 100% accuracy. [ABSTRACT FROM AUTHOR]

Abstract Among grains, rice is one of the most widely consumed cereals in the world; it represents a staple food in great part of Asia and Africa, and it is also broadly diffused in America and Europe. One of the main issues of storing rice is to protect it from animal attacks; in particular, it is prone to insect infestation. Despite all the attempts made to avoid it (developing new physical barriers, traps and repellants), often food pests manage to break into granary and parcels, contaminating stored commodities. As a consequence, possible infestations must be continuously checked by producers and/or retailers. Different methods have been developed to detect insects in stored commodities, and, despite some of them demonstrated to perform well, they present the substantial limitation of being destructive. This latter characteristic undoubtedly leads to an obvious loss of product (and consequently, of profit), affecting farmers, retailers, and, finally, consumers. For this reason, the aim of the present work is to develop a methodology for the identification of insect infestation in stored rice by NIR spectroscopy coupled with discriminant and modeling classification methods. In particular, among all the different pests possibly present in granaries, the focus has been on detection of the Indian-meal moth (Plodia interpunctella), because it is considered one of the most common infesting insects. Different samples of rice, both infested and edible, coming from different farmers located in six different Countries (Cambodia, India, Italy, Pakistan, Suriname and Thailand) have been analyzed by NIR spectroscopy. Consequently, two different classification methods, Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) have been applied in order to distinguish among infested and edible samples. In particular, PLS-DA allows correctly classifying 95.6% of the edible 97.5% of the contaminated samples (on the external validation set), whereas the SIMCA model, built only for the category of non-contaminated individuals, resulted highly specific (about 97%) but poorly sensitive on the test specimens. This latter approach (SIMCA) provided better predictions (in particular, in terms of sensitivity) when separate individual models were built subdividing samples in agreement with their country of origin. Highlights • Non-destructive approach for detecting insect infestation in rice • Coupling of NIR spectroscopy and chemometric classification techniques • Very good classification accuracy using partial least squares discriminant analysis • Class modeling (SIMCA) performs better on single-country data sets. [ABSTRACT FROM AUTHOR]

Natural defects, especially knots, on the surface of veneers have a great influence on the sorting and degradation of veneers. To realize rapid and accurate knot detection, a study on the possibility of detecting knots was carried out. Samples of poplar, eucalypt, and masson pine were used. The experiments mainly focused on the ability of using the models built with samples from one type of knot and normal wood to predict samples from a different type of knot and normal wood within the same wood species; and when only the samples from middle-sized knots and normal wood were used, whether or not the model based on one species could predict the samples from another species. The results showed that using the model built with small knots and normal wood to predict the larger knots and normal wood was not satisfactory, but the model based on large knots and normal wood can predict the samples from smaller knots and normal wood under a certain condition. When only the middle-sized knots and normal wood from the three species were used, the model built with eucalypt samples could predict the samples from poplar, and vice versa; however, the model built with masson pine samples could not predict the other two sample species, and vice versa.

Spectroscopy is widely used to characterize pharmaceutical products or processes, especially due to its desirable characteristics of being rapid, cheap, non-invasive/non-destructive and applicable both off-line and in-/at-/on-line. Spectroscopic techniques produce profiles containing a high amount of information, which can profitably be exploited through the use of multivariate mathematic and statistic (chemometric) techniques. The present paper aims at providing a brief overview of the different chemometric approaches applicable in the context of spectroscopy-based pharmaceutical analysis, discussing both the unsupervised exploration of the collected data and the possibility of building predictive models for both quantitative (calibration) and qualitative (classification) responses.