Estimation of age and sex from fingernail clippings by using ATR-FTIR spectroscopy coupled with chemometric interpretation.

Fingernails can act as important forensic evidence as they can be a source of DNA that may link the victim or accused to the crime scene and may also contain traces of drugs such as cocaine and heroin, in regular users. Moreover, previous studies have shown that analyzing fingernails with various techniques can reveal important information, such as age and sex. In this work, ATR-FTIR spectroscopy with chemometric tools has been used to estimate the age and sex from fingernails by analyzing 140 fingernail samples (70 males, and 70 females) collected from volunteers aged between 10 and 70 years old. The amide bands obtained from spectra confirmed the presence of keratin proteins in the samples. PCA and PLS-R were used for the classification of samples. For sex estimation, samples were divided into four categories based on age groups, followed by the differentiation of sex in each group. Similarly, for age estimation, all samples were divided into two sets based on male and female followed by differentiation of age groups in each set. The result showed that PLS-R was able to differentiate fingernail samples based on sex in groups G1, G2, G3, and G4 with R-square values of 0.972, 0.993, 0.991, and 0.996, respectively, and based on age in females, and males with R-square values of 0.93 and 0.97, respectively. External validation and blind tests were also performed which showed results with 100% accuracy. This approach has proved to be effective for the estimation of sex and age from fingernail samples.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

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.

Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study.

Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy.

Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses.

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose.

Prediction Accuracy of Soil Chemical Parameters by Field- and Laboratory-Obtained vis-NIR Spectra after External Parameter Orthogonalization.

One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO correction matrices based on the difference between spectra collected in situ and, respectively, spectra collected from the same soil samples after drying and sieving and after drying, sieving and finely grinding. Spectra from 134 soil samples recorded with two different spectrometers were split into calibration and validation sets and the two EPO corrections were applied. Clay, organic carbon and total nitrogen content were predicted by partial least squares regression for uncorrected and EPO-corrected spectra using models based on the same type of spectra ("within domain") as well as using laboratory-based models to predict in situ collected spectra ("cross-domain"). Our results show that the within-domain prediction of clay is improved with EPO corrections only for the research grade spectrometer, with no improvement for the other parameters. For the cross-domain predictions, there was a positive effect from both EPO corrections on all parameters. Overall, we also found that in situ collected spectra provided an equally successful prediction as laboratory-based spectra.

Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy.

OBJECTIVE: To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol. METHODS: Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively. RESULTS: As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model. CONCLUSIONS: Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach.

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Evaluation of the differences between low-salt solid-state fermented soy sauce and high-salt diluted-state fermented soy sauce in China: from taste-active compounds and aroma-active compounds to sensory characteristics.

BACKGROUND: The present study aimed to determine the components related to sensory properties in soy sauce and to characterize the differences between low-salt solid-state fermented soy sauce (LSFSS) and high-salt diluted-state fermented soy sauce (HDFSS). The taste and aroma active components of 18 commercially available soy sauces (eight types of LSFSS and 10 types of HDFSS) were characterized. The relationship between these compounds, soy sauce samples, and sensory properties was modeled by partial least squares regression. RESULTS: The analysis showed that the 11 taste-active components, including glutamic acid, glycine, alanine, threonine, malic acid, citric acid, tartaric acid, acetic acid, lactic acid, reducing sugar and salt, contributed greatly to the taste of soy sauce. In addition, umami, saltiness and sweetness are the characteristic tastes of HDFSS, whereas sourness and bitterness were the characteristic tastes of LSFSS. At the same time, seven aroma-active compounds, namely 4-ethyl-2-methoxyphenol, ethanol, 3-methyl-1-butanol, ethyl acetate, 2-phenethyl alcohol, 3-methyl thiopropanol and 2-ethyl-4-hydroxy-5-methylfuran-3-one, played a decisive role in the flavor of soy sauce. In addition, HDFSS presented the aroma attributes of smoky, alcoholic, floral, fruity and caramel-like, whereas LSFSS mainly presented sour and malty aroma attributes. CONCLUSION: The present study reveals new insight into the relationship between the chemical composition and sensory characteristics of soy sauce, which is of great significance for developing an objective measurement system and providing a theoretical basis to improve the sensory quality of soy sauce. © 2023 Society of Chemical Industry.

Characterization of Evodia rutaecarpa (Juss) Benth honey: volatile profile, odor-active compounds and odor properties.

BACKGROUND: Aroma is one of the most important quality criterion of different honeys and even defines their merchant value. The composition of volatile compounds, especially the characteristic odor-active compounds, contributes significantly to the aroma of honey. Evodia rutaecarpa (Juss) Benth honey (ERBH) is a special honey in China with unique flavor characteristics. However, no work in the literature has investigated the volatile compounds and characteristic odor-active compounds of ERBHs. Therefore, it is imperative to conduct systematic investigation into the volatile profile, odor-active compounds and odor properties of ERBHs. RESULTS: The characteristic fingerprint of ERBHs was successfully constructed with 12 characteristic peaks and a similarity range of 0.785-0.975. In total, 297 volatile compounds were identified and relatively quantified by headspace solid-phase microextraction coupled with gas chromatography quadrupole time-of-flight mass spectrometry, of which 61 and 31 were identified as odor-active compounds by relative odor activity values and GC-olfactometry analysis, respectively, especially the common 22 odor-active compounds (E)-ß-damascenone, phenethyl acetate, linalool, cis-linalool oxide (furanoid), octanal, hotrienol, trans-linalool oxide (furanoid), 4-oxoisophorone and eugenol, etc., contributed significantly to the aroma of ERBHs. The primary odor properties of ERBHs were floral, followed by fruity, herbaceous and woody aromas. The partial least-squares regression results showed that the odor-active compounds had good correlations with the odor properties. CONCLUSION: Identifying the aroma differences of different honeys is of great importance. The present study provides a reliable theoretical basis for the quality and authenticity of ERBHs. © 2023 Society of Chemical Industry.

Predicting leaf traits across functional groups using reflectance spectroscopy.

Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2  = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

Early detection of stripe rust infection in wheat using light-induced fluorescence spectroscopy.

In the current study, the application of fluorescence spectroscopy along with the advanced statistical technique and confocal microscopy was investigated for the early detection of stripe rust infection in wheat grown under field conditions. The indigenously developed Fluorosensor fitted with LED, emitting monochromatic light was used that covered comparatively larger leaf area for recording fluorescence data thus presenting more reliable current status of the leaf. The examined leaf samples covered the entire range of stripe rust disease infection from no visible symptoms to the complete disease prevalence. The molecular changes were also assessed in the leaves as the disease progresses. The emission spectra mainly produce two fluorescence emission classes, namely the blue-green fluorescence (400-600 nm range) and chlorophyll fluorescence (650-800 nm range). The chlorophyll fluorescence region showed lower chlorophyll bands both at 685 and 735 nm in the asymptomatic (early diseased) and symptomatic (diseased) leaf samples than the healthy ones as a result of partial deactivation of PSII reaction centers. The 735 nm chlorophyll fluorescence band was either slight or completely absent in the leaf samples with lower to higher disease incidence and thus differentiate between the healthy and the infected leaf samples. The Hydroxycinnamic acids (caffeic and sinapic acids) showed decreasing trend, whereas the ferulic acid increased with the rise in disease infection. Peak broadening/shifting has been observed in case of ferulic acid and carotenes/carotenoids, with the increase in the disease intensity. While using the LEDs (365 nm), the peak broadening and the decline in the chlorophyll fluorescence bands could be used for the early prediction of stripe rust disease in wheat crop. The PLSR statistical techniques discriminated well between the healthy and the diseased samples, thus showed promise in early disease detection. Confocal microscopy confirmed the early prevalence of stripe rust disease infection in a susceptible variety at a stage when the disease is not detectable visually. It is inferred that fluorescence emission spectroscopy along with the chemometrics aided in the effective and timely diagnosis of plant diseases and the detected signatures provide the basis for remote sensing.

Chemometric analysis applied to 1 H NMR and FTIR data for a quality parameter distinction of red fruit (Pandanus conoideus, lam.) oil products.

INTRODUCTION: Red fruit oil (RFO) is a natural product extracted from Pandanus conoideus Lam. fruit, a native plant from Papua, Indonesia. Recent studies indicate that RFO is popularly consumed as herbal medicine. Therefore, the quality of RFO must be assured. OBJECTIVES: This study aimed to develop a chemometric analysis applied to 1 H nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) data for important quality parameter distinction of red fruit oil (RFO), especially regarding the degree of unsaturation and the amount of free fatty acids (FFA). MATERIALS AND METHODS: Forty samples consisting of one crude RFO, thirty-three commercial RFOs, and three oils as blends, including olive oil, virgin coconut oil, and black seed oil, were analysed by 1 H NMR and FTIR spectroscopy. After appropriate preprocessing of the spectra, principal component analysis (PCA) and partial least squares regression (PLSR) were used for model development. RESULTS: The essential signals for modelling the degree of unsaturation are the signal at δ = 5.37-5.27 ppm (1 H NMR) and the band at 3000-3020 cm-1 (FTIR). The FFA profile represents the signal at δ = 2.37-2.20 ppm (1 H NMR) and the band at 1680-1780 cm-1 (FTIR). PCA allows the visualisation grouping on both methods with > 98% total principal component (PC) for the degree of unsaturation and > 88% total PC for FFA values. In addition, the PLSR model provides an acceptable coefficient of determination (R2 ) and errors in calibration, prediction, and cross-validation. CONCLUSION: Chemometric analysis applied to 1 H NMR and FTIR spectra of RFO successfully grouped and predicted product quality based on the degree of unsaturation and FFA value categories.

MYB Transcription Factor OsC1PLSr Involves the Regulation of Purple Leaf Sheath in Rice.

Although several regulators associated with purple traits in rice have been identified, the genetic basis of the purple sheath remains unclear. In the present study, F2-1 and F2-2 populations were constructed using purple sheath (H93S) and green sheath (R1173 and YHSM), respectively. In order to identify QTL loci in purple sheaths, BSA analyses were performed on the two F2 populations. A crucial QTL for purple sheath was identified, tentatively named qPLSr6, and was located in the 4.61 Mb to 6.03 Mb region of chromosome 6. Combined with expression pattern analysis of candidate genes, LOC_Os06g10350 (OsC1PLSr) was suggested as a candidate gene. The homozygous mutant KO-1 and KO-2 created through CRISPR/Cas9 editing, lost their purple leaf sheath. The RT-PCR revealed that OsC1PLSr, anthocyanin synthase (ANS), diflavonol-4-reductase (DFR), flavanone-3-hydroxylase (F3H), and flavanone-3'-hydroxylase (F3'H) expression levels were dramatically down-regulated in the mutants. The yeast report system indicated that the 145-272 aa region at the C-terminal of OsC1PLSr is a positive transcriptional activation domain. The results indicated that OsC1PLSr synthesized anthocyanins by regulating the expression of ANS, DFR, F3H, and F3'H. This study provides new insights into the genetic basis of the purple sheath.

Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy.

Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.

Rapid Determination of Nitrate Nitrogen Isotope in Water Using Fourier Transform Infrared Attenuated Total Reflectance Spectroscopy (FTIR-ATR) Coupled with Deconvolution Algorithm.

Nitrate is a prominent pollutant in water bodies around the world. The isotopes in nitrate provide an effective approach to trace the sources and transformations of nitrate in water bodies. However, determination of isotopic composition by conventional analytical techniques is time-consuming, laborious, and expensive, and alternative methods are urgently needed. In this study, the rapid determination of 15NO3- in water bodies using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with a deconvolution algorithm and a partial least squares regression (PLSR) model was explored. The results indicated that the characteristic peaks of 14NO3-/15NO3- mixtures with varied 14N/15N ratios were observed, and the proportion of 15NO3- was negatively correlated with the wavenumber of absorption peaks. The PLSR models for nitrate prediction of 14NO3-/15NO3- mixtures with different proportions were established based on deconvoluted spectra, which exhibited good performance with the ratio of prediction to deviation (RPD) values of more than 2.0 and the correlation coefficients (R2) of more than 0.84. Overall, the spectra pretreatment by the deconvolution algorithm dramatically improved the prediction models. Therefore, FTIR-ATR combined with deconvolution and PLSR provided a rapid, simple, and affordable method for determination of 15NO3- content in water bodies, which would facilitate and enhance the study of nitrate sources and water environment quality management.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil.

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.

Detection and Quantification of Adulteration in Krill Oil with Raman and Infrared Spectroscopic Methods.

Raman and infrared spectroscopy, used as individual and low-level fused datasets, were evaluated to identify and quantify the presence of adulterants (palm oil, PO; ω-3 concentrates in ethyl ester, O3C and fish oil, FO) in krill oil. These datasets were qualitatively analysed with principal component analysis (PCA) and classified as adulterated or unadulterated using support vector machines (SVM). Using partial least squares regression (PLSR), it was possible to identify and quantify the adulterant present in the KO mixture. Raman spectroscopy performed better (r2 = 0.98; RMSEP = 2.3%) than IR spectroscopy (r2 = 0.91; RMSEP = 4.2%) for quantification of O3C in KO. A data fusion approach further improved the analysis with model performance for quantification of PO (r2 = 0.98; RMSEP = 2.7%) and FO (r2 = 0.76; RMSEP = 9.1%). This study demonstrates the potential use of Raman and IR spectroscopy to quantify adulterants present in KO.

[Rapid detection technology of chemical component content in Lycii Fructus based on hyperspectral technology].

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Comparison of partial least square, artificial neural network, and support vector regressions for real-time monitoring of CHO cell culture processes using in situ near-infrared spectroscopy.

The biopharmaceutical industry must guarantee the efficiency and biosafety of biological medicines, which are quite sensitive to cell culture process variability. Real-time monitoring procedures based on vibrational spectroscopy such as near-infrared (NIR) spectroscopy, are then emerging to support innovative strategies for retro-control of key parameters as substrates and by-product concentration. Whereas monitoring models are mainly constructed using partial least squares regression (PLSR), spectroscopic models based on artificial neural networks (ANNR) and support vector regression (SVR) are emerging with promising results. Unfortunately, analysis of their performance in cell culture monitoring has been limited. This study was then focused to assess their performance and suitability for the cell culture process challenges. PLSR had inferior values of the determination coefficient (R2 ) for all the monitored parameters (i.e., 0.85, 0.93, and 0.98, respectively for the PLSR, SVR, and ANNR models for glucose). In general, PLSR had a limited performance while models based on ANNR and SVR have been shown superior due to better management of inter-batch heterogeneity and enhanced specificity. Overall, the use of SVR and ANNR for the generation of calibration models enhanced the potential of NIR spectroscopy as a monitoring tool.

Characterization of different non-Saccharomyces yeasts via mono-fermentation to produce polyphenol-enriched and fragrant kiwi wine.

To improve the functional property and flavor quality of kiwi wine, the performance of 11 strains of non-Saccharomyces yeasts from 5 species were comprehensively characterized in kiwi wine. Chemical compositions and sensorial profiles of all kiwi wines were assessed. The results indicated that most non-Saccharomyces cerevisiae produced more polyphenols than Saccharomyces cerevisiae WLS21 (Sc21). A total of 130 volatiles were observed in the kiwi wines. Zygosaccharomyces rouxii IFO30 (Zr30), Zygosaccharomyces bailii IFO37 (Zb37) and Schizosaccharomyces pombe 1757 (Sp57) were found to produce more concentration of volatile compounds than the other strains including Sc21. 25 volatiles with a rOAV ≥0.1 were identified. Principal component analysis (PCA) revealed that Zr30 and Zb37 specifically increased the concentrations of ethyl esters, 2-methylbutan-1-ol and phenethyl acetate, while Sp57 primarily enhanced the contents of phenylacetaldehyde, 2-methylbutan-1-ol and phenethyl acetate. The sensory analysis demonstrated that Zr30 and Zb37 strains were more optimal than S. cerevisiae in aroma generation. In addition, the partial least-squares regression (PLSR) analysis revealed that tropical fruits, red fruits, dried fruits, flowers and floral odors showed an intensely positive impact on the overall acceptability of the kiwi wine.