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We report the fabrication and characterisation of near-IR reflecting films and coatings based on shear-assembled crystalline ensembles of polymer composite microspheres, also known as “polymer opals”. Extension of the emulsion polymerisation techniques for synthesis of tractable larger core-interlayer-shell (CIS) particles, of up to half a micron diameter, facilitates the engineering and processing of thin-film synthetic opals, with a tunable photonic stopband spanning an extended spectral range of λ ≈ 700–1600 nm. Samples exhibit strong “scattering cone” interactions, with considerable angular dependence and angle tuning possible, as measured with a goniometric technique. These intense optical resonances in the near-IR, particularly within the important region around λ ~ 800 nm, combined with an appreciable translucency within the visible light spectrum, is indicative of the potential applications in coatings technologies and solar cells.

Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C. auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts’ metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.

Despite the recommendation to treat depression using transcranial direct current stimulation (tDCS), novel findings raise doubts over the tDCS’s efficacy in managing depressive episodes. Neurophysiologic approaches to understanding the specificities of brain responses to tDCS in patients with depression remain to be explored. Objective: Our aim was to compare immediate hemodynamic responses to tDCS on the left dorsolateral prefrontal cortex (DLPFC; F3-Fp2 montage) in patients with depressive disorder and in controls (no additional stimuli). Methods: Sixteen participants were allocated to the depression group and sixteen to the control group. Both groups received 2 mA tDCS for 20 min, using the F3-Fp2 montage. The hemodynamic effect over the DLPFC was assessed using functional near-infrared intracranial spectroscopy (fNIRS) positioned on the left supraorbital region (Fp1). Mean, minimal, and maximal values of baseline and post-stimulation rates of oxygen saturation (SatO2) were recorded. The oxygenated hemoglobin rates (HbO) were extracted. Results: Between-group differences were detected for minimal baseline rates of SatO2 and HbO levels. The depression group showed lower results compared to the control group at baseline. After the protocol, only the depression group showed increased minimal rates of SatO2 and HbO. The post-tDCS minimal rates were equal for both groups. Conclusions: The findings showed immediate anodal tDCS effects over DLPFC hemodynamics. The effects were exclusive to the lowest baseline rate group and did not affect the normal oxygen rate group. The minimal increase in SatO2 and HbO rates after the protocol in the depression group suggests that those with reduced cerebral perfusion may be more affected by tDCS.

The Q-LAC analyzer, a portable device employing near-infrared spectroscopy (NIRS), was designed to measure muscle oxygen saturation (SmO2) during physical exercise. This study aimed to assess the reliability of the Q-LAC analyzer in determining SmO2 levels during incremental cycling exercise. Thirteen physically active males (age 28.1 ± 5.3 y; height 181.2 ± 5.7 cm; body mass 79.9 ± 11.1 kg; BMI 24.2 ± 2.4 kg/m2) participated in this study. A submaximal incremental exercise test (SIET) on an electromagnetic cycle ergometer with a seven-day interval was performed twice. SmO2 levels in the vastus lateralis (VL) muscle of the dominant leg were simultaneously recorded using the Q-LAC device during both tests. The study calculated the intraclass correlation coefficient (ICC), the coefficient of variation (CV), the standard error of measurement (SEM), the smallest worthwhile change (SWC), and mean detectable change (MDC). A within-within-subjects ANOVA revealed no statistically significant effects for session (F1,12 = 0.97, p = 0.34, η2 = 0.07) and the interaction between session and workload (F4,48 = 0.19, p = 0.94, η2 = 0.02). ICC values ranged from 0.72 to 0.91. Furthermore, the analysis of CV, SEM, and SWC indicated that SmO2 measurements obtained with the Q-LAC device exhibit good reliability but marginal sensitivity. In conclusion, the portable Q-LAC analyzer provides consistent measurements of muscle oxygen saturation during low-to-moderate-intensity exercise on a cycle ergometer.

Liquor has a complex system with high dimensional components. The trace components in liquor are varied and have low content and complex coordination relationships. This study aimed to solve the problem of reliance on smell and taste. Based on the characteristics of near-infrared spectrum response to hydrogen-containing groups, qualitative analysis was carried out in combination with machine learning technology. Firstly, an iterative adaptive weighted penalized least squares algorithm with spectral peak discrimination was used for baseline correction to effectively retain useful information in the feature absorption peaks. Then, the convolution smoothing algorithm was used to filter the noise, and the spectral curve smoothness was adjusted using the convolution window width. The near-infrared spectrum has a high dimension. Monte Carlo random sampling combined with an improved competitive adaptive reweighting method was used to evaluate the importance of spectral sampling points. According to the importance coefficient, the dimension of the spectral data set was optimized by using an exponential attenuation function through an iterative operation, and the data set with the smallest root-mean-square error was taken as the characteristic spectrum. The nonlinear separability of characteristic spectra was further improved by kernel principal component analysis. Finally, a liquor quality recognition model based on principal component analysis was established by using the hierarchical multiclass support vector machine method. Our key findings revealed that the prediction accuracy of the model reached 96.87% when the number of principal components was 5–12, with more than 95% of the characteristic information retained. These results demonstrated that this rapid nondestructive testing method resolved the challenge posed by relying on subjective sensory evaluation for liquor analysis. The findings provide a reliable analytical approach for studying substances with high-dimensional component characteristics.

Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection.

Regional oxygen saturation (rSO2) assessed by near-infrared spectroscopy (NIRS) reflects the perfusion and metabolism of the assessed tissue. The study aimed to determine the reference values of rSO2 for selected body areas, considering gender, age and body composition. We studied 70 healthy volunteers divided into two age groups (18–30 and >60 years). The rSO2 was measured using NIRS in eighteen selected regions of interest (ROIs). Body composition analysis was carried out using dual-energy X-ray absorptiometry (DXA). Significant differences in rSO2 values were found between almost all analyzed ROIs (p < 0.05) with a simultaneous lack of asymmetry between contralateral side of the body. The average rSO2 values from the ROIs analyzed ranged from 40.34 ± 17.65% (Achilles tendon) to 69.94 ± 6.93% (tibialis anterior muscle). Age and the values of adiposity indices and the fat mass content are factors that may significantly reduce the rSO2 value. In most ROIs, higher rSO2 values were recorded for the younger group (p < 0.0001). The rSO2 values at rest are area-specific in young and elderly healthy subjects. The changes in rSO2, both in clinical assessment and research, should be interpreted taking into account the body area being assessed and individual factors such as age and body fat content.

Hemodynamic models provide a mathematical representation and computational framework that describe the changes in blood flow, blood volume, and oxygenation levels that occur in response to neural activity and systemic changes, while near-infrared spectroscopy (NIRS) measures deoxyhemoglobin, oxyhemoglobin, and other chromophores to analyze cerebral hemodynamics and metabolism. In this study, we apply a dynamic hemometabolic model to NIRS data acquired during cardiac arrest and cardiopulmonary resuscitation (CPR) in pigs. Our goals were to test the model’s ability to accurately describe the observed phenomena, to gain an understanding of the intricate behavior of cerebral microvasculature, and to compare the obtained parameters with known values. By employing the inverse of the hemometabolic model, we measured a range of significant physiological parameters, such as the rate of oxygen diffusion from blood to tissue, the arteriole and venule volume fractions, and the Fåhraeus factor. Statistical analysis uncovered significant differences in the baseline and post-cardiac arrest values of some of the parameters.

During the production process of some copper clad laminate (CCL), the content of 3,3,5,7,7-pentamethyl-1,2,4-trioxepane in prepreg can affect the cross-linking and curing degree of resin directly, and thereby affect the properties of CCL. In this article, near-infrared (NIR) spectroscopy combined with partial least square regression using high performance liquid chromatography as a reference method were used for the determination of 3,3,5,7,7-pentamethyl-1,2,4-trioxepane in the CCL production progress. 160 spectra of prepreg samples randomly formed the calibration set, and 50 spectra of prepreg samples formed the validation set. The value of the root mean square error of calibration (w/w) and root mean square error of prediction (w/w) were 0.011% and 0.013% for 3,3,5,7,7-pentamethyl-1,2,4-trioxepane content (%, w/w), and the calculation and validation of the regression equation resulted in high correlation coefficients of 0.99 and 0.98, respectively. The scatter plot value of calibration set which was obtained from the root mean square error of cross-validation (w/w) was 0.015%, and the regression equation resulted in high correlation coefficient of 0.98. The results of the paired t-test revealed that there was no significant difference between NIR and HPLC method. Thus, the results obtained in this study reflect that NIR could be used as a rapid, accurate, and simultaneous technique to determine 3,3,5,7,7-pentamethyl-1,2,4-trioxepane content of prepreg in the production process.

Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R2c) for the training set and (R2p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.

In this work, the solid–liquid extraction of bioactive molecules from grape skin was performed using water as the extraction solvent. The effects of extraction time (t = 60, 75, and 90 min), extraction temperature (T = 40, 60, and 80 °C), solid–liquid phase ratio (S/L = 10, 20, and 30 g/L), and mixing speed (rpm = 250, 500, and 750 1/min) on the total dissolved solids, extraction yield, concentration of total polyphenols, and antioxidant activity were determined using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) methods. According to response surface modeling, the optimal extraction conditions were t = 75 min, T = 80 °C, S/L = 30 g/L, and rpm = 750 1/min, and under optimal process conditions, 8.38 mgGAE/gd.m. was obtained. Furthermore, the potential of near-infrared (NIR) spectroscopy coupled with artificial neural network (ANN) modeling for prediction of the physical and chemical properties of prepared extracts was also analyzed. The use of ANN modeling demonstrated highly favorable correlations between the NIR spectra and all the variables tested, particularly the total dissolved solids (TDS) and antioxidant activity measured using the FRAP method. As a result, ANN modeling proved to be a valuable tool for predicting the concentration of total polyphenols, the antioxidant activity, and the extraction yield of a plant extract based on its NIR spectra.

The current innovative work combines nano-optical sensors with near-infrared spectroscopy for rapid detection and quantification of polyphenols and investigates the potential of the nano-optical sensor based on chemo-selective colorants to detect the dynamic changes in aroma components during the fermentation of tea extract. The procedure examined the influence of different ultrasound-assisted sonication factors on the changes in the consumption rate of polyphenols during the fermentation of tea extract versus non-sonication as a control group. The results showed that the polyphenol consumption rate improved under the ultrasound conditions of 28 kHz ultrasound frequency, 24 min treatment time, and 40 W/L ultrasonic power density. The metal–organic framework based nano-optical sensors reported here have more adsorption sites for enhanced adsorption of the volatile organic compounds. The polystyrene-acrylic microstructure offered specific surface area for the reactants. Besides, the employed porous silica nanospheres with higher porosity administered improved gas enrichment effect. The nano-optical sensor exhibits good performance with a “chromatogram” for the identification of aroma components in the fermentation process of tea extract. The proposed method respectively enhanced the consumption rate of polyphenol by 35.57%, 11.34% and 16.09% under the optimized conditions. Based on the established polyphenol quantitative prediction models, this work demonstrated the feasibility of using a nano-optical sensor to perform in-situ imaging of the fermentation degree of tea extracts subjected to ultrasonic treatment.

Lead chalcogenide nanocrystals (NCs) are an emerging class of photoactive materials that have become a versatile tool for fabricating new generation photonics devices operating in the near-IR spectral range. NCs are presented in a wide variety of forms and sizes, each of which has its own unique features. Here, we discuss colloidal lead chalcogenide NCs in which one dimension is much smaller than the others, i.e., two-dimensional (2D) NCs. The purpose of this review is to present a complete picture of today’s progress on such materials. The topic is quite complicated, as a variety of synthetic approaches result in NCs with different thicknesses and lateral sizes, which dramatically change the NCs photophysical properties. The recent advances highlighted in this review demonstrate lead chalcogenide 2D NCs as promising materials for breakthrough developments. We summarized and organized the known data, including theoretical works, to highlight the most important 2D NC features and give the basis for their interpretation.

The implementation of a suitable and ecologically friendly solid waste management plan is accepted as an essential need. Given that organic matter constitutes the majority of solid waste, composting has gained popularity as an alternative way of organic refuse recycling. Compost quality is defined by its stability and maturity, both of which must be assessed by measuring a large number of physical–chemical parameters, microbiological variables, and enzymatic activities. These procedures are complex and time-consuming, making it difficult to assess compost quality correctly. Spectroscopy methods could be used as an efficient alternative. In this work, general information about composting processes and near-infrared spectroscopy (NIRS) is given. A discussion and comparison of the different approaches of coupling NIRS and chemometric tools for the monitoring and/or control of composting processes are presented in this work.

Near-infrared (NIR) spectroscopy is widely used for non-destructive detection of fruit quality, but the transferability of NIR models between different fruits is still a challenge. This study investigates the transferability of NIR models from strawberry to grape and apple using two case studies. A total of 94 strawberry, 80 grape, and 125 apple samples were measured for their soluble solids content (SSC) and NIR spectra. Partial least squares (PLS) regression was used to establish a model for predicting strawberry SSC, with an acceptable root mean square error of prediction (RMSEP) and correlation coefficient (R) of 0.53 °Brix and 0.91, respectively. Directly applying the strawberry model to grape and apple spectra significantly degrades the performance, increasing the RMSEP up to 3.47 and 16.40, respectively. Spectral preprocessing can improve the predictions for all three fruits, but the bias cannot be eliminated. Global modeling produces a generalized model, but the prediction for strawberry degrades. Calibration transfer with SS-PFCE and PLS correction, which are calibration methods without standard samples, was found to be an effective way to improve the prediction of grape and apple spectra using the strawberry model. Therefore, calibration transfer may be a feasible way for improving the transferability of NIR models for multiple fruits.

Abstract Human serum globulin (GLB), which contains various antibodies in healthy human serum, is of great significance for clinical trials and disease diagnosis. In this study, the GLB in human serum was rapidly analyzed by near infrared (NIR) spectroscopy without chemical reagents. Optimal partner wavelength combination (OPWC) method was employed for selecting discrete information wavelength. For the OPWC, the redundant wavelengths were removed by repeated projection iteration based on binary linear regression, and the result converged to stable number of wavelengths. By the way, the convergence of algorithm was proved theoretically. Moving window partial least squares (MW-PLS) and Monte Carlo uninformative variable elimination PLS (MC-UVE-PLS) methods, which are two well-performed wavelength selection methods, were also performed for comparison. The optimal models were obtained by the three methods, and the corresponding root-mean-square error of cross validation and correlation coefficient of prediction (SECV, RP,CV) were 0.813 g L−1 and 0.978 with OPWC combined with PLS (OPWC-PLS), and 0.804 g L−1 and 0.979 with MW-PLS, and 1.153 g L−1 and 0.948 with MC-UVE-PLS, respectively. The OPWC-PLS and MW-PLS methods achieved almost the same good results. However, the OPWC only contained 28 wavelengths, so it had obvious lower model complexity. Thus it can be seen that the OPWC-PLS has great prediction performance for GLB and its algorithm is convergent and rapid. The results provide important technical support for the rapid detection of serum.

Rapid determination of the mechanical performance of coniferous wood has great importance for wood processing and utilization. Near-infrared spectroscopy (NIRS) is widely used in various production fields because of its high efficiency and non-destructive characteristics, however, the traditional NIR spectroscopy analysis techniques mainly focus on the spectral pretreatment and dimension reduction methods, which are difficult to maximize use of effective spectral information and are time consuming and laborious. Deep learning methods can automatically extract features; data-driven artificial intelligence technology can discover the internal correlation between data and realize many detection tasks in life and production. In this paper, we propose a SE-DenseNet model, which can realize end-to-end prediction without complex spectral dimension reduction compared with traditional modeling methods. The experimental results show that the proposed SE-DenseNet model achieved classification accuracy and F1 values of 88.89% and 0.8831 on the larch’s test set, respectively. The proposed SE-DenseNet model achieved correlation coefficients (R) and root mean square errors (RMSE) of 0.9144 and 1.2389 MPa on the larch’s test set, respectively. Implementation of this study demonstrates that SE-DenseNet can realize automatic extraction of spectral features and the accurate determination of wood mechanical properties.

Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.

The objective of this study was to develop and validate a near-infrared (NIR) spectroscopy based method for in-line quantification during the second alcohol precipitation process of Astragali radix. In total, 22 calibration experiments were carefully arranged using a Box–Behnken design. Variations in the raw materials, critical process parameters, and environmental temperature were all included in the experimental design. Two independent validation sets were built for method evaluation. Validation set 1 was used for optimization. Different spectral pretreatments were compared using a “trial-and-error” approach. To reduce the calculation times, the full-factorial design was applied to determine the potential optimal combinations. Then, the best parameters for the pretreatment algorithms were compared and selected. Partial least squares (PLS) regression models were obtained with low complexity and good predictive performance. Validation set 2 was used for a thorough validation of the NIR spectroscopy method. Based on the same validation set, traditional chemometric validation and validation using accuracy profiles were conducted and compared. Conventional chemometric parameters were used to obtain the overall predictive capability of the established models; however, these parameters were insufficient for pharmaceutical regulatory requirements. Then, the method was fully validated according to the ICH Q2(R1) guideline and using the accuracy profile approach, which enabled visual and reliable representation of the future performances of the analytical method. The developed method was able to determine content ranges of 8.44–39.8% at 0.541–2.26 mg/mL, 0.118–0.502 mg/mL, 0.220–0.940 mg/mL, 0.106–0.167 mg/mL, 0.484–0.879 mg/mL, and 0.137–0.320 mg/mL for total solid, calycosin glucoside, formononetin glucoside, 9, 10-dimethoxypterocarpan glucopyranoside, 2′-dihydroxy -3′, 4′-dimethoxyisoflavan glucopyranoside, astragloside II, and astragloside IV, respectively. These ranges were specific to the early and middle stages of the second alcohol precipitation process. The method was confirmed to be capable of achieving an in-line prediction with a very acceptable accuracy. The present study demonstrates that accuracy profiles offer a potential approach for the standardization of NIR spectroscopy method validation for traditional Chinese medicines (TCMs).

We present a novel, noninvasive, super-continuum infrared spectroscopy of cytochrome c oxidase (SCISCCO) system for simultaneously measuring hemodynamic and metabolic parameters, and we demonstrate its utility by applying it to lab calibration tests, human studies, and swine animal studies. The system optically assays the redox state of cytochrome c oxidase (CCO), as well as traditional markers including oxygenated (HbO) and deoxygenated (HbR) hemoglobin. To demonstrate in vivo feasibility, the measured responses of oxygenation and CCO responses to acute ischemia on the arm and forehead in human participants are compared to data from the literature. The validated SCISCCO system is then applied in human studies to measure cerebral oxygenation and the redox state of CCO in participants during an attention test protocol. We show that the redox state of CCO and hemodynamics measured by the SCISCCO system are consistent with the physiological hypothesis established in prior studies. To enable use of the SCISCCO system in laboratory and hospital settings as well as transportation to remote locations, a cart-based SCISCCO prototype system has also been developed. The cart-based SCISCCO prototype is applied to swine animal models undergoing induction of hemorrhagic shock followed by partial resuscitative endovascular balloon occlusion of the aorta (pREBOA). The pilot study demonstrates the feasibility of using the SCISCCO instrument within the context of existing protocols and validates the instrument’s measurements against the physiological and hemodynamic parameters measured by other conventional devices.

The Romanian economy is dominated by linear value-chains [...]

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

A major limitation of near-infrared spectroscopy (NIRS) is its high sensitivity to the scalp and low sensitivity to the brain of adult humans. In the present work we used multi-distance hyperspectral NIRS (hNIRS) to investigate the optimal source-detector distances, wavelength ranges, and analysis techniques to separate cerebral responses to 30 s breath-holds (BHs) from the responses in the superficial tissue layer in healthy adult humans. We observed significant responses to BHs in the scalp hemodynamics. Cerebral responses to BHs were detected in the cytochrome C oxidase redox (rCCO) at 4 cm without using data from the short-distance channel. Using the data from the 1 cm channel in the two-layer regression algorithm showed that cerebral hemodynamic and rCCO responses also occurred at 3 cm. We found that the waveband 700–900 nm was optimal for the detection of cerebral responses to BHs in adults.

This study investigated the effects of rowing with different seat cushion and cadence conditions on oxyhemoglobin (O2Hb) and total hemoglobin (tHb) levels of the erector spinae (ES) as well as the effects on heart rate (HR) and ratings of perceived exertion (RPE). Thirty healthy adults completed tests under three unstable air seat cushion pressure levels (0, 80, and 140 mmHg) and three rowing cadences (slow: 18 bpm, medium: 30 bpm, and fast: 36 bpm) on a rowing machine, for a total of nine test conditions. During the exercise period, rowing on cushions set to 80 mmHg resulted in greater O2Hb and tHb changes than did rowing at 0 mmHg (p < 0.05). When rowing cadence increased, the O2Hb and tHb decreased during the exercise period, whereas HR and RPE increased (p < 0.05). During the recovery period, O2Hb and tHb on cushions set to 140 mmHg during slow rowing were higher than those at 0 mmHg during slow rowing and 140 mmHg during fast rowing (p < 0.05). Rowing on an appropriate pressure of seat cushion and using a slow cadence contribute to increasing muscle oxygenation of low back during exercise.

An efficient, versatile and non-destructive in situ method in reaction monitoring using vibrational spectroscopy is described. A Suzuki cross-coupling reaction was monitored in which the substrate 1-iodo-2-nitrobenzene reacted with the electrophile phenylboronic acid to form the product 2-nitrobiphenyl. To hasten the reaction, palladium(II) acetate and potassium carbonate were added to serve as a catalyst and to promote transmetalation, respectively. This reaction was monitored using near-infrared and Raman spectroscopy. The recorded data was subjected to multivariate data analysis such as principal component analysis in order to detect spectral changes signalling formation of the product. To confirm the presence of the desired product, offline analyses were performed using gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. The results demonstrate how Raman spectroscopy is able to detect the formation of the product in real time, whereas near-infrared spectroscopy fails to do so.

Near-infrared spectroscopy (NIRS) is a non-ionizing optical technique that can be used to quantify proteins, carbohydrates, fats, and other organic and biological substances. The aim of this study was to determine the ability of NIRS to identify different concentrations of L-fucose and L-proline solutions by utilizing different NIR spectral regions. NIR spectra of solid L-fucose and L-proline, their aqueous solutions in different concentrations, and the spectra of saliva samples collected from two patients with oral squamous cell carcinoma (OSCC) were studied. Differences in spectra of the pure solid reference samples and water were most noticeable in spectral regions 800–1250 nm and 1418–1867 nm. The saliva sample with an atypically high concentration of oral cancer biomarkers showed a similar spectral feature between 1530–1650 nm as the liquid samples with cancer biomarkers. In addition, a fine k-nearest neighbors (kNN) classifier was trained to differentiate the aqueous solutions and achieved 75.97% validation accuracy. The preliminary study presents that NIRS can be utilized to detect differences in spectra between the different biomarker concentrations in aqueous solutions. However, the qualitative measures may have resulted in limited sensitivity, which could be enhanced by additional samples and using a measurement probe dedicated to fluid measurements.

The altered glucose metabolism characterising cancer cells determines an increased amount of methylglyoxal in their secretome. Previous studies have demonstrated that the methylglyoxal, in turn, modifies the protonation state (PS) of soluble proteins contained in the secretomes of cultivated circulating tumour cells (CTCs). In this study, we describe a method to assess the content of methylglyoxal adducts (MAs) in the secretome by near-infrared (NIR) portable handheld spectroscopy and the extreme learning machine (ELM) algorithm. By measuring the vibration absorption functional groups containing hydrogen, such as C-H, O-H and N-H, NIR generates specific spectra. These spectra reflect alterations of the energy frequency of a sample bringing information about its MAs concentration levels. The algorithm deciphers the information encoded in the spectra and yields a quantitative estimate of the concentration of MAs in the sample. This procedure was used for the comparative analysis of different biological fluids extracted from patients suspected of having cancer (secretome, plasma, serum, interstitial fluid and whole blood) measured directly on the solute left on a surface upon a sample-drop cast and evaporation, without any sample pretreatment. Qualitative and quantitative regression models were built and tested to characterise the different levels of MAs by ELM. The final model we selected was able to automatically segregate tumour from non-tumour patients. The method is simple, rapid and repeatable; moreover, it can be integrated in portable electronic devices for point-of-care and remote testing of patients.

Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.

The rapid determination of nitrogen, phosphorus, potassium and other major nutrient elements is an important technical guarantee in the quality control of chemical fertilizers. In this study, a small visible spectrometer and a small near-infrared spectrometer were used to collect spectrum information of 33 different common chemical fertilizers including compound fertilizers, blended fertilizers and controlled-release fertilizers. The 550~950 nm and 1050~1640 nm spectra with stable signals were intercepted as the analysis spectrum, and the competitive adaptive reweighted sampling algorithm (CARS) was used to select 161, 229, and 161 spectral characteristic wavelengths for the three nutrient contents of N, P2O5, and K2O respectively. The partial least squares (PLS) and extreme learning machine (ELM) models of N, P2O5, and K2O were established based on the 550~950 nm waveband, 1050~1640 nm waveband, full spectrum, and characteristic wavelength, respectively. The coefficient of determination (R2), root mean square error (RMSE), and residual predictive deviation (RPD) were used to evaluate the effect of the model. With the optimal prediction models, the values of Rp2 for N, P2O5, and K2O were 0.989, 0.963, 0.981, and for RPD were 9.71, 5.09, 7.29, respectively. The research results show that Vis/NIR spectroscopy can predict the content of nitrogen, phosphorus, and potassium nutrients in fertilizers, and the near-infrared band from 1050 nm to 1640 nm has a better prediction effect. The characteristic wavelength selection reduces the spectral variables by 9/10, and the performance of the model based on the characteristic wavelength is close to that of the full-spectrum model.

The use of standard laboratory methods for trait evaluation is expensive and challenging, especially for low-resource breeding programs. For carotenoid assessment, rather than the standard HPLC method, these programs mostly rely on proxy approaches for quantitative total carotenoid content (TCC) assessment. To ensure data transferability and consistency, calibration models were developed using TCC iCheck and Chroma Meter proxy methods for the adoption of the alternative near-infrared phenotyping method in cassava. Calibration was developed for dry matter content (DMC) using a simple and inexpensive sampling procedure associated with the proxy TCC protocols. The partial least square (PLS) and random forest (RF) models were compared for the two traits, and the correlation (r) between the actual and predicted values in the training and validation (in bracket) sets of r = 0.85 (0.76) and r = 0.98 (0.82) with PLS and RF, respectively, for iCheck, and r = 0.99 (0.96) and r = 0.99 (0.96) with PLS and RF, respectively, for Chroma Meter, was obtained. The calibration result of r = 0.93 (0.83) and r = 0.99 (0.81) using PLS and RF, respectively, was obtained for DMC. This effort is valuable in carotenoids improvement and supports the ongoing effort in adopting portable spectrometers for rapid and cheap phenotyping in cassava.

Perfusion-related information is reportedly embedded in the low-frequency component of a blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal. The blood-propagation pattern through the cerebral vascular tree is detected as an interregional lag variation of spontaneous low-frequency oscillations (sLFOs). Mapping of this lag, or phase, has been implicitly treated as a projection of the vascular tree structure onto real space. While accumulating evidence supports the biological significance of this signal component, the physiological basis of the “perfusion lag structure,” a requirement for an integrative resting-state fMRI-signal model, is lacking. In this study, we conducted analyses furthering the hypothesis that the sLFO is not only largely of systemic origin, but also essentially intrinsic to blood, and hence behaves as a virtual tracer. By summing the small fluctuations of instantaneous phase differences between adjacent vascular regions, a velocity response to respiratory challenges was detected. Regarding the relationship to neurovascular coupling, the removal of the whole lag structure, which can be considered as an optimized global-signal regression, resulted in a reduction of inter-individual variance while preserving the fMRI response. Examination of the T2* and S0, or non-BOLD, components of the fMRI signal revealed that the lag structure is deoxyhemoglobin dependent, while paradoxically presenting a signal-magnitude reduction in the venous side of the cerebral vasculature. These findings provide insight into the origin of BOLD sLFOs, suggesting that they are highly intrinsic to the circulating blood. [ABSTRACT FROM AUTHOR]

A high work of breathing can compromise limb oxygen delivery during sustained high-intensity exercise. However, it is unclear if the same is true for intermittent sprint exercise. This project examined the effect of adding an inspiratory load on locomotor muscle tissue reoxygenation during repeated-sprint exercise. Ten healthy males completed three experiment sessions of ten 10-s sprints, separated by 30-s of passive rest on a cycle ergometer. The first two sessions were “all-out’ efforts performed without (CTRL) or with inspiratory loading (INSP) in a randomised and counterbalanced order. The third experiment session (MATCH) consisted of ten 10-s work-matched intervals. Tissue saturation index (TSI) and deoxy-haemoglobin (HHb) of the vastus lateralis and sixth intercostal space was monitored with near-infrared spectroscopy. Vastus lateralis reoxygenation (ΔReoxy) was calculated as the difference from peak HHb (sprint) to nadir HHb (recovery). Total mechanical work completed was similar between INSP and CTRL (effect size: -0.18, 90% confidence limit ±0.43), and differences in vastus lateralis TSI during the sprint (-0.01 ±0.33) and recovery (-0.08 ±0.50) phases were unclear. There was also no meaningful difference in ΔReoxy (0.21 ±0.37). Intercostal HHb was higher in the INSP session compared to CTRL (0.42 ±0.34), whilst the difference was unclear for TSI (-0.01 ±0.33). During MATCH exercise, differences in vastus lateralis TSI were unclear compared to INSP for both sprint (0.10 ±0.30) and recovery (-0.09 ±0.48) phases, and there was no meaningful difference in ΔReoxy (-0.25 ±0.55). Intercostal TSI was higher during MATCH compared to INSP (0.95 ±0.53), whereas HHb was lower (-1.09 ±0.33). The lack of difference in ΔReoxy between INSP and CTRL suggests that for intermittent sprint exercise, the metabolic O2 demands of both the respiratory and locomotor muscles can be met. Additionally, the similarity of the MATCH suggests that ΔReoxy was maximal in all exercise conditions. [ABSTRACT FROM AUTHOR]

Late-onset sepsis is frequently seen in preterm infants and is associated with poor neurodevelopmental outcome. White matter damage is proposed as substrate of poor outcome, with contributing factors as regional hypoxia and effects of cytokines on oligodendrocytes. We investigated the relation between cerebral oxygenation during (suspected) late-onset sepsis and neurodevelopmental outcome. Prospective cohort study, including preterm infants (gestational age <32 weeks and/or birthweight <1500 grams) with (suspected) late-onset sepsis underwent NIRS registration during the first 72 hours of suspected late-onset sepsis. At two years corrected age neurodevelopment was scored using the Bayley Scales of Infant Development-II. Thirty-two infants were included. Twenty-seven infants were identified with proven late-onset sepsis and five infants had clinical sepsis without positive blood culture. In this study, late-onset sepsis was predominantly caused by coagulase negative staphylococci (CoNS) (72%). All NIRS values were within normal limits. No association was found between NIRS and impaired neurodevelopmental outcome (n = 4) at corrected age two years: composite cognitive score 105 (80–115), composite motor score 103 (82–118) (median and range). In this pilot study, late-onset sepsis (predominantly caused by CoNS with a relatively mild clinical course), was not associated with aberrant NIRS values, nor with impaired neurodevelopmental outcome. Further research might establish our findings and elucidate effects of other micro-organisms on cerebral perfusion. [ABSTRACT FROM AUTHOR]

A thorough understanding of the heritability, genetic correlations and additive and non-additive variance components of tree growth and wood properties is a requisite for effective tree breeding. This knowledge is essential to maximize genetic gain, that is, the amount of increase in trait performance achieved annually through directional selection. Understanding the genetic attributes of traits targeted by breeding is also important to sustain decade-long genetic progress, that is, the progress made by increasing the average genetic value of the offspring as compared to that of the parental generation. In this study, we report quantitative genetic parameters for fifteen growth, wood chemical and physical traits for the world-famous Eucalyptus urograndis hybrid (E. grandis × E. urophylla). These traits directly impact the optimal use of wood for cellulose pulp, paper, and energy production. A population of 1,000 trees sampled in a progeny trial was phenotyped directly or following the development and use of near-infrared spectroscopy calibration models. Trees were genotyped with 33,398 SNPs and 24,001 DArT-seq genome-wide markers and genomic realized relationship matrices (GRM) were used for parameter estimation with an individual-tree additive-dominant mixed model. Wood chemical properties and wood density showed stronger genetic control than growth, cellulose and fiber traits. Additive effects are the main drivers of genetic variation for all traits, but dominance plays an equally or more important role for growth, singularly in this hybrid. GRM´s with >10,000 markers provided stable relationships estimates and more accurate parameters than pedigrees by capturing the full genetic relationships among individuals and disentangling the non-additive from the additive genetic component. Low correlations between growth and wood properties indicate that simultaneous selection for wood traits can be applied with minor effects on genetic gain for growth. Conversely, moderate to strong correlations between wood density and chemical traits exist, likely due to their interdependency on cell wall structure such that responses to selection will be connected for these traits. Our results illustrate the advantage of using genome-wide marker data to inform tree breeding in general and have important consequences for operational breeding of eucalypt urograndis hybrids. [ABSTRACT FROM AUTHOR]

Investigation of marine mammal dive-by-dive blood distribution and oxygenation has been limited by a lack of noninvasive technology for use in freely diving animals. Here, we developed a noninvasive near-infrared spectroscopy (NIRS) device to measure relative changes in blood volume and haemoglobin oxygenation continuously in the blubber and brain of voluntarily diving harbour seals. Our results show that seals routinely exhibit preparatory peripheral vasoconstriction accompanied by increased cerebral blood volume approximately 15 s before submersion. These anticipatory adjustments confirm that blood redistribution in seals is under some degree of cognitive control that precedes the mammalian dive response. Seals also routinely increase cerebral oxygenation at a consistent time during each dive, despite a lack of access to ambient air. We suggest that this frequent and reproducible reoxygenation pattern, without access to ambient air, is underpinned by previously unrecognised changes in cerebral drainage. The ability to track blood volume and oxygenation in different tissues using NIRS will facilitate a more accurate understanding of physiological plasticity in diving animals in an increasingly disturbed and exploited environment. [ABSTRACT FROM AUTHOR]

Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain distributions of parameters that can better guard against misinterpretation of results, as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a ‘healthy’ and ‘impaired’ state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies described impaired microvascular perfusion and tissue oxygenation as reliable predictors of Multiple Organ Failure in major trauma. However, this relationship has been incompletely investigated. The objective of this analysis is to further evaluate the association between organ dysfunction and microcirculation after trauma. Materials and methods: This is a retrospective subgroup analysis on 28 trauma patients enrolled for the Microcirculation DAIly MONitoring in critically ill patients study (NCT 02649088). Patients were divided in two groups according with their Sequential Organ Failure Assessment (SOFA) score at day 4. At admission and every 24 hours, the sublingual microcirculation was evaluated with Sidestream Darkfield Imaging (SDF) and peripheral tissue perfusion was assessed with Near Infrared Spectroscopy (NIRS) and Vascular Occlusion Test (VOT). Simultaneously, hemodynamic, clinical/laboratory parameters and main organ supports were collected. Results: Median SOFA score at Day 4 was 6.5. Accordingly, patients were divided in two groups: D4-SOFA ≤6.5 and D4-SOFA >6.5. The Length of Stay in Intensive Care was significantly higher in patients with D4-SOFA>6.5 compared to D4-SOFA≤6.5 (p = 0.013). Total Vessel Density of small vessels was significantly lower in patients with high D4-SOFA score at Day 1 (p = 0.002) and Day 2 (p = 0.006) after admission; the Perfused Vessel Density was lower in patients with high D4-SOFA score at Day 1 (p = 0.007) and Day 2 (p = 0.033). At Day 1, NIRS monitoring with VOT showed significantly faster tissue oxygen saturation downslope (p = 0.018) and slower upslope (p = 0.04) in patients with high D4-SOFA. Discussion: In our cohort of major traumas, sublingual microcirculation and peripheral microvascular reactivity were significantly more impaired early after trauma in those patients who developed more severe organ dysfunctions. Our data would support the hypothesis that restoration of macrocirculation can be dissociated from restoration of peripheral and tissue perfusion, and that microvascular alterations can be associated with organ failure. [ABSTRACT FROM AUTHOR]

Microbial rhodopsins have become an important tool in the field of optogenetics. However, effective in vivo optogenetics is in many cases severely limited due to the strong absorption and scattering of visible light by biological tissues. Recently, a combination of opsin site-directed mutagenesis and analog retinal substitution has produced variants of proteorhodopsin which absorb maximally in the near-infrared (NIR). In this study, UV-Visible-NIR absorption and resonance Raman spectroscopy were used to study the double mutant, D212N/F234S, of green absorbing proteorhodopsin (GPR) regenerated with MMAR, a retinal analog containing a methylamino modified β-ionone ring. Four distinct subcomponent absorption bands with peak maxima near 560, 620, 710 and 780 nm are detected with the NIR bands dominant at pH <7.3, and the visible bands dominant at pH 9.5. FT-Raman using 1064-nm excitation reveal two strong ethylenic bands at 1482 and 1498 cm-1 corresponding to the NIR subcomponent absorption bands based on an extended linear correlation between λmax and γC = C. This spectrum exhibits two intense bands in the fingerprint and HOOP mode regions that are highly characteristic of the O640 photointermediate from the light-adapted bacteriorhodopsin photocycle. In contrast, 532-nm excitation enhances the 560-nm component, which exhibits bands very similar to light-adapted bacteriorhodopsin and/or the acid-purple form of bacteriorhodopsin. Native GPR and its mutant D97N when regenerated with MMAR also exhibit similar absorption and Raman bands but with weaker contributions from the NIR absorbing components. Based on these results it is proposed that the NIR absorption in GPR-D212N/F234S with MMAR arises from an O-like chromophore, where the Schiff base counterion D97 is protonated and the MMAR adopts an all-trans configuration with a non-planar geometry due to twists in the conjugated polyene segment. This configuration is characterized by extensive charge delocalization, most likely involving nitrogens atoms in the MMAR chromophore. [ABSTRACT FROM AUTHOR]

Boswellia sacra, an endemic tree to Oman, is exposed to man-made incisions for commercial level frankincense production, whereas unsustainable harvesting may lead to population decline. In this case, assessment of endogenous phytohormones (gibberellic acid (GA), indole-acetic acid (IAA), salicylic acid (SA) and kinetin) can help to understand population health and growth dynamics. Hence, it was aimed to devise a robust method using Near-Infrared spectroscopy (NIRS) coupled with multivariate methods for phytohormone analysis of thirteen different populations of B. sacra. NIRS data was recorded in absorption mode (10000–4000 cm-1) to build partial least squares regression model (calibration set 70%). Model was externally cross validated (30%) as a test set to check their prediction ability before the application to quantify the unknown amount of phytohormones in thirteen different populations of B. sacra. The results showed that phytohormonal contents varied significantly, showing a trend of SA>GA/IAA>kinetin across different populations. SA and GA contents were significantly higher in Pop13 (Hasik), followed by Pop2 (Dowkah)–an extreme end of B. sacra tree cover in Dhofar region. A similar trend in the concentration of phytohormones was found when the samples from 13 populations were subjected to advance liquid chromatography mass spectrophotometer and gas chromatograph with selected ion monitor analysis. The current analysis provides alternative tool to assess plant health, which could be important to in situ propagation of tree population as well as monitoring tree population growth dynamics. [ABSTRACT FROM AUTHOR]

Under the influence of complex environmental conditions, the spatial heterogeneity of soil organic matter (SOM) is inevitable, and the relationship between SOM and visible and near-infrared (VNIR) spectra has the potential to be nonlinear. However, conventional VNIR-based methods for soil organic matter estimation cannot simultaneously consider the potential nonlinear relationship between the explanatory variables and predictors and the spatial heterogeneity of the relationship. Thus, the regional application of existing VNIR spectra-based SOM estimation methods is limited. This study combines the proposed partial least squares–based multivariate adaptive regression spline (PLS–MARS) method and a regional multi-variable associate rule mining and Rank–Kennard-Stone method (MVARC-R-KS) to construct a nonlinear prediction model to realize local optimality considering spatial heterogeneity. First, the MVARC-R-KS method is utilized to select representative samples and alleviate the sample global underrepresentation caused by spatial heterogeneity. Second, the PLS–MARS method is proposed to construct a nonlinear VNIR spectra-based estimation model with local optimization based on selected representative samples. PLS–MARS combined with the MVARC-R-KS method is illustrated and validated through a case study of Jianghan Plain in Hubei Province, China. Results showed that the proposed method far outweighs some available methods in terms of accuracy and robustness, suggesting the reliability of the proposed prediction model.

The brain tissue partial oxygen pressure (PbtO2) and near-infrared spectroscopy (NIRS) neuromonitoring are frequently compared in the management of acute moderate and severe traumatic brain injury patients; however, the relationship between their respective output parameters flows from the complex pathogenesis of tissue respiration after brain trauma. NIRS neuromonitoring overcomes certain limitations related to the heterogeneity of the pathology across the brain that cannot be adequately addressed by local-sample invasive neuromonitoring (e.g., PbtO2 neuromonitoring, microdialysis), and it allows clinicians to assess parameters that cannot otherwise be scanned. The anatomical co-registration of an NIRS signal with axial imaging (e.g., computerized tomography scan) enhances the optical signal, which can be changed by the anatomy of the lesions and the significance of the radiological assessment. These arguments led us to conclude that rather than aiming to substitute PbtO2 with tissue saturation, multiple types of NIRS should be included via multimodal systemic- and neuro-monitoring, whose values then are incorporated into biosignatures linked to patient status and prognosis. Discussion on the abnormalities in tissue respiration due to brain trauma and how they affect the PbtO2 and NIRS neuromonitoring is given.

Taro (Colocasia esculenta (L.) Schott) and sweet potato (Ipomoea batatas (L.) Lam.) are important food crops worldwide, whose productivity is threatened by climatic constraints, namely drought. Data calibration, validation, and model development of high-precision near-infrared spectroscopy (NIRS) involving multivariate analyses are needed for the fast prediction of the quality of tubers and shoots impacted by drought stress. The main objective of this study was to generate accurate NIRS models for quality assessment of taro and sweet potato accessions (acc.) subjected to water scarcity conditions. Seven taro and eight sweet potato acc. from diverse geographical origins were evaluated for nitrogen (N), protein (Pt), starch (St), total mineral (M), calcium oxalate (CaOx), carbon isotope discrimination (Δ13C), and nitrogen isotopic composition (δ15N). Models were developed separately for both crops underground and aboveground organs. N, Pt, St, and M models could be used as quality control constituents, with a determination coefficient of prediction (r2pred) between 0.856 and 0.995. δ13C, δ15N, and CaOx, with r2pred between 0.178 and 0.788, could be used as an informative germplasm screening tool. The approach used in the present study demonstrates NIRS’s potential for further research on crop quality under drought.

Taking a variety of edible oils as the research object, including soybean oil, peanut oil, rapeseed oil, a method based on Near-Infrared Spectroscopy (NIRS) to identify the frying times is proposed to evaluate the quality of frying oil. Ten rounds of frying experiments are carried out for each of the three oils. The spectra of the first eight rounds are used to build the model, and the last two are used for model testing. First, all the original spectra are preprocessed using the first derivative (1D). Then, the correlation coefficient between the sequence of frying times and absorbance is calculated, and the characteristic wavelengths with a high correlation coefficient are extracted. Finally, a differential prediction model is established based on the characteristic wavelengths. The results show that the differential prediction model accurately predicts the frying times of various edible oils and provides a new method for quality inspection of frying oil, and the predicted accuracy of the frying times of three frying oils is 100% within the allowable range of error.