Partial Least Squares Discriminant Analysis Research Articles

In-house validation of a visible and near infrared spectroscopy non-targeted method to support panel test of virgin olive oils

In this study, an in-house validation of Visible and Near Infrared Spectroscopy was performed to distinguish between extra virgin olive oil (EVOO) and virgin olive oil (VOO). A total of 161 samples of olive oil of three different categories (EVOO, VOO and lampante (LOO)) were analysed by transflectance using a monochromator instrument. One-class models were initially developed using Partial Least Squares (PLS) Density Modelling to characterize EVOO and VOO category. Once the LOO samples were discriminated, linear and non-linear discriminant models were built to classify EVOO and VOO. Different data pre-treatments and variable selection algorithms were evaluated to establish the best models in terms of Correct Classification Rate (CCR). The best model, obtained after variable selection using PLS Discriminant Analysis, yielded CCR values of 82.35 % for EVOO and 66.67 % for VOO in external validation. These results confirmed that VIS + NIRS technology may be used to provide rapid, non-destructive preliminary screening of olive oil samples for categorization; suspect samples may then be analysed by official analytical methods.

Metabolism changes caused by glucose in normal and cancer human brain cell lines by Raman imaging and chemometric methods

Glucose is the main source of energy for the human brain. This paper presents a non-invasive technique to study metabolic changes caused by glucose in human brain cell lines. In this paper we present the spectroscopic characterization of human normal brain (NHA; astrocytes) and human cancer brain (CRL-1718; astrocytoma and U-87 MG; glioblastoma) control cell lines and cell lines upon supplementation with glucose. Based on Raman techniques we have identified biomarkers that can monitor metabolic changes in lipid droplets, mitochondria and nucleus caused by glucose. We have studied the vibrations at 750 cm−1, 1444 cm−1, 1584 cm−1 and 1656 cm−1 as a function of malignancy grade. We have compared the concentration of cytochrome, lipids and proteins in the grade of cancer aggressiveness in normal and cancer human brain cell lines. Chemometric analysis has shown that control normal, control cancer brain cell lines and normal and cancer cell lines after supplementation with glucose can be distinguished based on their unique vibrational properties. PLSDA (Partial Least Squares Discriminant Analysis) and ANOVA tests have confirmed the main role of cytochromes, proteins and lipids in differentiation of control human brain cells and cells upon supplementation with glucose. We have shown that Raman techniques combined with chemometric analysis provide additional insight to monitor the biology of astrocytes, astrocytoma and glioblastoma after glucose supplementation.

Empowering the rapid authentication of the botanical origin of monofloral honey by coated blade spray mass spectrometry (CBS-MS)

In the quest for a rapid and cost-effective tool for determining the botanical origin of monofloral honey, the analytical capabilities of coated blade spray mass spectrometry (CBS-MS) were investigated. To this aim, the chemical profiles of 64 honey samples from seven different botanical origins (acacia, dandelion, chestnut, rhododendron, citrus, sunflower, and linden) were captured by the absorbent of the coated blades and then analyzed by mass spectrometry. An exploratory analysis was performed by principal component analysis (PCA) to generate a graphical representation of the CBS-MS data that allows the discovery of patterns, outliers, or relations between types of honey in an unsupervised manner. Additionally, the performances of four different classification algorithms (least absolute shrinkage and selection operator (LASSO), random forest (RF), and neural network (NNET) partial least squares discriminant analysis (PLS-DA)) were built up and compared. The performances of the four classifiers were verified by a 50 times-repeated, 5-fold-cross-validation and permutation test. Although all classifiers performed well, the RF showed significantly higher performances in cross-validation (with area under the curve (AUC) of 0.99, overall accuracy 0.94, Kappa 0.93, sensitivity 0.94, and specificity 0.99). Moreover, the permutation tests showed the models were not overfitted. Finally, to determine the molecular identities of the ions that most contribute to the classification, extracts from the same honey samples were prepared, analyzed by liquid chromatography coupled to high resolution tandem mass spectrometry (LC-HRMS/MS), and the most significant features were annotated. This proof-of-principle work warrants a future large-scale study to validate and challenge this CBS-MS-based method with a greater number of honeys from different years and geographical origins.

Evaluation of adulteration in soy-based beverages by water addition using chemometrics applied to ATR-FTIR spectroscopy

The adulteration of soy-based beverages (SBBs) by adding water to increase profitability is a fraudulent practice that requires urgent solutions to ensure product integrity and consumer trust. Therefore, the use of infrared spectroscopy (ATR-FTIR) associated with chemometrics methods can be a quick and advantageous alternative to this problem. In this study, the one-class and multiclass methods applied to ATR-FTIR data to classify a set of 80 SBBs samples were used. The unequal dispersed classes (UNEQ), soft independent modeling of class analogy (SIMCA), data driven SIMCA (DD-SIMCA), and one-class random forest (OC-RF) methods were used for one-class modeling. Models were constructed using the non-adulterated samples as target class (TA) and the adulterated samples as non-target class (NT). The k-nearest neighbors (k-NN), partial least squares discriminant analysis (PLS-DA), dual class random forest (DC-RF), and dual class random forest with Monte Carlo sampling (DC-RF-MC) methods were used for multiclass modeling. For k-NN and PLS-DA, samples were organized into four classes (non-adulterated samples, adulterated with 5% v∙v−1, 10% v∙v−1, and 20% v∙v−1 of water). DC-RF models used the same class settings as one-class models. DD-SIMCA, PLS-DA, and DC-RF-MC showed accuracy of 100%. The results show the feasibility of ATR-FTIR and chemometrics models to identify adulterations by adding water.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds

Rapid identification of soybean seed varieties is crucial for agricultural production and seed quality. Identifying varieties of soybean seed using conventional chemical methods is time-consuming, destructive, and inappropriate for seed quality evaluation. This study utilized hyperspectral imaging technology (HSI) to identify four varieties of soybean seeds. The hyperspectral images of soybean seeds were collected in the spectral range of 400–1000 nm. A multi-level data fusion strategy based on spectral and image information was proposed to improve the accuracy of model. Subsequently, the multi-level data fusion strategy based on partial least squares discriminant analysis (PLS-DA) was used to establish the classification models of soybean seeds. Compared with the models using individual analytical sources, the results demonstrated that the models with multi-level data fusion strategy obtained better prediction performance. The high-level data fusion (HLDF) based on Bayesian consensus provided the optimal results with an accuracy (Acc) and F1-score of 93.13 % and 93.70 % in the prediction phase, respectively. Therefore, the multi-level data fusion strategy can be used as an identification method for soybean seed varieties and an effective approach to enhance the discriminatory capability of models.

Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning.

Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.

Residual Mulching Film Detection in Seed Cotton Using Line Laser Imaging

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film’s semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing.

Light-driven incubation of Fusarium species and near-infrared spectroscopy for an early in vitro identification of Fusarium circinatum

This study explores the application of near-infrared (NIR) spectroscopy coupled with multivariate techniques for the rapid and accurate identification of four Fusarium species, with emphasis on the detection of Fusarium circinatum, the causative agent of “Pitch canker” disease in forests, cultured in vitro under distinct light conditions. The impact of two different incubation conditions, near-ultraviolet (NUV) and warm light, on the spectral characteristics of cultures was used as a strategy to improve the early fungal detection using spectroscopy. Spectral data were acquired using a portable NIR spectrometer at 48, 72, and 96 h post-cultivation from various zones of mycelial growth. The results showed distinct spectral profiles among Fusarium species, with notable variations depending on incubation light conditions and growth zones. NUV light conditions significantly improved species differentiation, especially in the central and middle zones of mycelial growth at 72 h post-cultivation (hpc). Partial least squares − discriminant analysis (PLS-DA) model under NUV light conditions provided the best identification strategy, achieving correct classification rates over 97% for all four species from 72 hpc and 82.5% of correct classification of F. circinatum. These findings demonstrate the effectiveness of the NIRS methodology supported with multivariate techniques and combined with optimization of light conditions and mycelial growth zone of cultures, for the early and rapid detection of Fusarium species in culture media, with potential implications for plant pathology diagnostics and disease management strategies.

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis–NIR–SWIR hyperspectral imaging

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis–NIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis–NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis–NIR spectral data.

DART-HRMS reveals metabolic changes of whey through microparticulation and fermentations

Despite native whey (hereafter termed WHEY) being a watery by-product rich in high-value nutritional components, it poses environmental concerns. Recently, the techniques of thermal-mechanical microparticulation to produce microparticulated whey (MPW) and a further process to ferment MPW (FMPW) were suggested, in order to recycle whey into a protein concentrated soft dairy cream that acquires also fat mimicking functionality. Aiming at monitoring the effects of this recycling process on the products’ metabolic profiles, samples (n = 8) of WHEY, MPW and two sub-types of FMPW were analysed by combining direct analysis in real time coupled with high resolution mass spectrometry (DART-HRMS). The most informative ions were used to build a partial least squared discriminant analysis (PLS-DA) model to assess the biochemical compounds that characterize and differentiate WHEY from MPW and FMPW. The DART-HRMS fingerprints acquired in negative mode accurately differentiated samples throughout the steps of microparticulation and fermentation. Post-fermentation, greater relative abundances of lactic acid, glucosamine and histidyl-aspartic acid were detected in both subtypes of FMPW, which were moderately differentiated by DART-HRMS. Moreover, while WHEY and MPW contained high relative abundances of hydroxyglutaric and malic acids, the fermented derivates (FMPW) were characterised by elevated levels of volatile compounds (tetradecanal, hexadecene and tetradecene) and fermentative end-products (diethyltartrate and histidyl-aspartic acid). DART-HRMS successfully captured a pool of informative biomarkers useful to understand the chemical and metabolomic changes occurring in microparticulation and fermentation processes used to recycle whey. This analytical technique is an operative supporting step needed for comprehensive assessment of the nutritional and organoleptic properties of the proposed whey-based soft cream dairy food.

Comparison of qualitative and quantitative performance of two portable near-infrared spectrometers for intact Rehmanniae Radix and calibration transfer

As a Chinese herbal medicine with high medical value, Rehmanniae Radix (RR) has a wide variety of geographical origins leading to distinctly diverse quality, and moisture content during storage affects the critical active ingredient content. In this work, two portable near-infrared (NIR) spectrometers, DLP NIRscan Nano (DLP) and VIAVI MicroNIR 1700 (M1700) were used for in-situ spectral acquisition from intact RR. The partial least squares-discriminant analysis (PLS-DA) was employed for three geographical sources (Shandong, Shanxi, Henan). After multiple pre-processing and wavelength selection, the M1700-based PLS-DA model, accuracy (Acc), sensitivity (Sen), and specificity (Spe), achieved 100 % accuracy. In contrast, model predictions using the low-cost instrument (DLP) was not satisfactory. The partial least squares regression (PLSR) was applied to predict the moisture content of RR. The best correlation coefficients in the prediction set (R2p), Root Mean Square Error of Prediction (RMSEP), and residual prediction deviation (RPD) values (0.98, 0.98, 4.98) were obtained by DLP and were also employed for comparison with the M1700 model (0.99, 0.70, 7.00). Therefore, to further improve the model prediction effect of the low-cost DLP, we employed the improved principal component analysis (IPCA), direct standardization (DS), and piecewise direct standardization (PDS) calibration transfer techniques. Unequivocally, IPCA optimized the origin identification model (Acc, Sen, and Spe were all 1.00), the moisture content prediction model RPD was increased by 23 %, and its RMSEP was reduced by 18 %, leading to the improvement in prediction accuracy of the DLP model. This study provides a portable and low-cost detection method for the in-situ evaluation of the quality of RR quality and a feasible solution for the NIR techniques to be used in the rapid and accurate in-situ analysis of RR.

ATR‐FTIR Spectroscopy Preprocessing Technique Selection for Identification of Geographical Origins of Gastrodia elata Blume

ABSTRACTGastrodia elata Blume from different regions varies in growth conditions, soil types, and climate, which directly affects the content and quality of its medicinal components. Accurately identifying the origin can effectively ensure the medicinal value of G. elata Bl., prevent the circulation of counterfeit products, and thus protect the interests and health of consumers. Attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy is a rapid and effective method for verifying the authenticity of traditional Chinese medicines. However, the presence of scattering effects in the spectra poses challenges in establishing reliable discrimination models. Therefore, employing appropriate scattering correction techniques is crucial for improving the quality of spectral data and the accuracy of discrimination models. This study uses two ensemble preprocessing approaches; the first type is series fusion of scatter correction technologies (SCSF), and another method is sequential preprocessing through orthogonalization (SPORT). Four discriminant models were established using a single scattering correction technique and two ensemble preprocessing approaches. The results show that the data‐driven version of the soft independent modeling of class analogy (DD‐SIMCA) model built based on multiplicative scatter correction (MSC) preprocessing has a sensitivity of 0.98 and a specificity of 0.91, able to effectively distinguish whether a sample of G. elata Bl. originates from Zhaotong. In addition, three discriminant models including support vector machine (SVM), partial least squares discriminant analysis (PLS‐DA), and three gradient boosting machine (GBM) algorithms built using the ensemble preprocessing approach have good classification and generalization capabilities. Among them, the SCSF‐PLS‐DA model has the best performance with 99.68% and 98.08% accuracy for the training and test sets, respectively, and F1 of 0.97; the SPORT‐SVM model achieved the second‐best classification ability. The results show that the ensemble preprocessing approach used can improve the success rate of G. elata Bl. geographical origin classification.

O-281 Characterization of Vaginal microbiota and reproductive outcomes associated with assisted reproductive technologies through next-generation sequencing, from Indian population

Abstract Study question Is there any association between the composition of the vaginal microbiota and the reproductive outcomes in infertile women’s undergoing for assisted reproductive treatments (ART)? Summary answer Higher microbial abundance in non-pregnant patients compared to pregnant patients. These pathogens could be used as a possible marker for a higher IVF failure rate. What is known already Various next- generation sequencing methods have been used to study bacterial populations in infertile Women. Vaginal microbiota could have an influence on success rates of fertility treatments. The bacterial microbiota varies by the population. There is no direct evidence on this subject to guide clinical decision making or its influence on the hierarchy of fertility treatments. While the presence of pathogenic bacteria non-Lactobacilli dominated microbiota is associated with poor reproductive outcomes in assisted reproductive treatments (ART). Additional studies, particularly those using metagenomics approaches, may be useful. Study design, size, duration A total of 240 infertile women’s were included in the study. The study population consists of patients attending to the ART unit of a tertiary medical center Udaipur, India, between November 2021 to October 2023. Depending on the biochemical pregnancy results, they were divided into two groups. Group 1 (n = 128) was pregnancy (P), and Group 2 (n = 112) non- pregnancy (NP) group, Patients were aged between (20-45 years). The ethical approval was obtained from the institute. Participants/materials, setting, methods DNA was extracted from vaginal swab samples collected at the time of oocyte pick up using the commercially available ( DNeasy Power Soil kit). The Vaginal microbiota of samples were analyzed using 16S ribosomal RNA gene amplification (MinION) Oxford Nanopore Ltd. Bioinformatics analysis was performed using QIIME2 and Microbiome Analyst packages. Alpha, beta diversity and taxonomic characterization were compared for microbial composition. The findings of the principle coordinate analysis were shown using R version 4.0. Main results and the role of chance Our results show that higher Shannon and Simpson index were obtained in the non- pregnancy (NP) group compared to the pregnancy (P) group. In addition, the partial least squares discriminant analysis (PLS-DA) revealed a difference in microbial composition between pregnancies (P) and non- pregnancy (NP) groups, as well as a higher microbial abundance in non-pregnant patients compared to pregnant patients. At the phylum level Firmicutes, Actinobacteria, Proteobacteria, Fusobacteria, and Bacteroidetes are the most common predominant in the pregnancy (P) group (80.77, 9.52, 9.18, 0.04, and 0.40%, respectively) and the non- pregnancy (NP) group (72, 61, 12.41, 8.01, and 3.75%). A higher abundance of Firmicutes (80.77 vs. 72.61%) and Proteobacteria (9.18 vs. 8.01%), and a lower abundance of Actinobacteria (9.52 vs. 12.41%, p = 0.03), Fusobacteria (0.04 vs. 8.01%, p = 0.02), and Bacteroidetes (0.40 vs. 3.75%, p = 0.03) were observed in pregnancy (P) group. At the genus level, pregnancy group (P) increased the abundance of probiotics lactic acid bacteria (74.61 vs 63.09%) and decreased the abundance of the pathogen Gardnerella (6.03 vs 7.24%, p = 0.03), Atopobium (0.84 vs 4.14%, p = 0.02), Sneathia (0.03 vs. 3.75%, p = 0.02), and Prevotella (0.38 vs. 3, 02%, p = 0.04). Limitations, reasons for caution The limited sample size is the primary constraint of this study. Larger studies with a larger sample size are needed to confirm the distinct microbiome patterns identified in female reproductive tracts samples in connection to infertility and live birth rate (LBR) outcomes. Wider implications of the findings Vaginal microbiota composition associated to different clinical outcomes in patients undergoing ART might have profound implications. This may lead to failed fertility treatments and reduced live birth rate (LBR). More studies are needed to clarify the relative effectiveness of treatments for infertility. Trial registration number NA

Systematic metabolomics study in the serum and urine of a mouse model of Fabry disease.

Fabry disease (FD) is an X-linked lysosomal disorder caused by α-galactosidase A enzyme activity deficiency. Although glycosphingolipid analogs have been identified in the plasma or urine of patients with FD, there is a limited understanding of altered metabolomics profiles beyond the globotriaosylceramide accumulation in FD. Metabolomics study was performed for monitoring of biomarker and altered metabolism related with disease progression in serum and urine from male α-galactosidase A knockout mice and age-matched wild-type mice at 20 and 40 weeks. Profiling analysis for metabolites, including organic acids, amino acids, fatty acids, kynurenine pathway metabolites, and nucleosides in the serum and urine was performed using gas chromatography-tandem mass spectrometry and liquid chromatography-tandem mass spectrometry combined with star symbol patterns and partial least squares discriminant analysis (PLS-DA). A total of 27 and 23 metabolites from the serum and urine of Fabry mice were distinguished from those of wild-type mice, respectively, based on p-value (<0.05) and variable importance in projection scores (>1.0) of PLS-DA. In the serum, metabolites of the glutathione, glutathione disulfide, citrulline, and kynurenine pathways that are related to oxidative stress, nitric oxide biosynthesis, and inflammation were increased, whereas those involved in pyruvate and tyrosine metabolism and the tricarboxylic acid cycle were altered in the 20- and 40-week-old urine of FD model mice. Altered metabolic signatures associated with disease progression by oxidative stress, inflammation, nitric oxide biosynthesis, and immune regulation in the early and late stages of FD.

Flavor Variations in Precious Tricholoma matsutake under Different Drying Processes as Detected with HS-SPME-GC-MS.

By employing headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), this study displayed the compositional changes in volatile organic compounds (VOCs) in Tricholoma matsutake samples subjected to hot-air drying (HAD) and vacuum freeze-drying (VFD) processes from their fresh samples. A total of 99 VOCs were detected, including 2 acids, 10 aldehydes, 10 alcohols, 13 esters, 12 ketones, 24 alkanes, 14 olefins, 7 aromatic hydrocarbons, and 7 heterocyclic compounds. Notably, the drying process led to a decrease in most alcohols and aldehydes, but an increase in esters, ketones, acids, alkanes, olefins, aromatic, and heterocyclic compounds. Venn diagram (Venn), principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) analyses enabled an easy and rapid distinction between the VOC profiles of T. matsutake subjected to different drying methods. Among the identified VOCs, 30 were designated as marker VOCs indicative of the employed drying process. And the VFD method was more capable of preserving the VOCs of fresh T. matsutake samples than the HAD method. Benzaldehyde, 1-Octen-3-ol, 3-Octanol, and (E)-2-Octen-1-ol were identified as markers for FRESH T. matsutake. Conversely, (E)-3-Hexene, lavender lactone, and α-Pinene were associated with VFD T. matsutake. For HAD T. matsutake, olefins, pyrazine, and esters, particularly ocimene, 2,5-Dimethyl-pyrazine, and methyl cinnamate, significantly contributed to its particularities. The results from this present study can provide a practical guidance for the quality and flavor control of volatile organic compounds in preciously fungal fruiting bodies by using drying processes.

Generation of Olfactory Compounds in Cat Food Attractants: Chicken Liver-Derived Protein Hydrolysates and Their Contribution to Enhancing Palatability.

The present study investigated the impact of four chicken liver protein hydrolysate-based cat food attractants on palatability. Aroma compounds were analyzed in these attractants, which were subsequently sprayed onto four different types of cat foods. Results revealed that CF4 exhibited the highest intake ratio and the first choice ratio, followed by CF2 sample. Orthogonal partial least-squares discriminant analysis (OPLS-DA) demonstrated significant differences among 50 volatile compounds identified from the four cat foods. Using variable importance in projection (VIP) values, we selected 17 key flavor compounds responsible for distinguishing between the four cat foods. Peptides with a molecular mass <180 Da showed correlation with nonanoic acid and cedrol, while those >3000 Da correlated with hexanoic acid ethyl ester. Regression coefficients (RCs) calculated from partial least-squares regression (PLSR) results showed positive correlations between compound content and palatability for six compounds, whereas negative correlations were observed for ten compounds. Validation experiments confirmed that nonanal, 2-propylpyridine, and 3-octen-2-one enhanced palatability and correlated with peptides ranging from 180 to 500 Da; conversely, nonanoic acid ethyl ester and 3-methyl-pentanoic acid reduced palatability and correlated with peptides ranging from 1000 to 3000 Da.

Discrimination and analytical profiling of colored printed documents using ATR-FTIR spectroscopy coupled with explorative and predictive statistical analysis: Part I

Printed documents are a common form of evidence in forensic document examination. The integration of spectroscopy with chemometrics have evolved evidential analytical interpretation of printing inks. However, we report the first ever study that explores the examination of both black and colored printed documents combined with explorative Principal Component Analysis (PCA) and supervised techniques viz. Soft independent modelling of class analogy (SIMCA) and Partial Least Square- Discriminant Analysis (PLS-DA). The study investigated 74 (40 Ink-based and 34 Toner- based) colored printed document samples using ATR-FTIR to discriminate and determine the source of origin of an unknown printed document using a non-destructive approach. Qualitative analysis by ATR- FTIR indicated the presence of polystyrene, bisphenol A and acrylates as the common binder polymers in the samples. The study was also able to obtain pigment information like presence of PR 57 and PR 146 in magenta, Carbon black in black, Copper Phthalocyanine and PB 15 in Cyan and PY 74 in yellow colored printed samples. Further, PCA has been used as an explorative technique that showed a variance of 97 % in the dataset and indicating that the color Cyan contributes to the maximum classification accuracy. SIMCA has been used as a supervised method to classify the known and test samples to their respective defined classes. However, SIMCA could only classify Toner-based samples in their respective class and inconclusive results were obtained in case of Ink-based samples. Finally, PLS-DA was also used to classify the two class of samples which resulted in a discrimination accuracy of 98.6 %. The derived model was also used for validation study on blind test samples which provided 100 % classification results.

Near-infrared spectroscopy identification method of cashmere and wool fibers based on an optimized wavelength selection algorithm

Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.

Identification and Quantitative Analysis of the Pharmacologically Active Components of Jianxin Granules by UHPLC-QTOF-MS/MS-Based Metabolomics

Jianxin Granules, a Traditional Chinese Medicine (TCM), consisting of eight flavors, including Huang Qi (astragalus), Hong Shen (red ginseng), Pu Huang (pollen typhae), Dan Shen (salvia miltiorrhiza), Zhu Ling (polyporus), Bai Zhu (atractylodes macrocephala), Gui Zhi (cassia twig), Ting Li Zi (semen lepidii). Jianxin granules has a multi-system, multi-target, and multi-directional comprehensive regulatory effect on inhibiting ventricular remodeling, which is an effective formulation for the prevention and treatment of heart failure, and has a good application prospect. However, many of the ingredients, including pharmacologically active ingredients, in the Jianxin granules remain unclear. Here, we attempted to develop a metabolomics method of component identification, quantitation, pattern recognition, and cross-comparison of Jianxin granules. Chemical analysis, component identification and quantification analyse of Jianxin granules were conducted with a combination of UHPLC-QTOF-MS/MS with bioinformatics. Assessment of the correlation between technical and bio-replicated pharmacological active ingredients was implemented by Principal Component Analysis (PCA), in addition to Partial Least Squares Discriminant Analysis (PLS-DA). UHPLC-QTOFMS/ MS, a metabolomics method, was developed and adapted to characterize Jianxin granules, which consisted of 178 to 216 molecular signatures. The quantitative analysis of 95 frequently occurring molecular signatures of Jianxin granules was carried out by a single exogenous reference internal standard. Of these, 47 have been identified using diverse databases, including 2 glycosylglycerol derivatives, 2 lipids, 2 spiro compounds, 2 cyclohexanecarboxylic acids, 2 glycosides, 5 terpenoids, 7 oligopeptides, 17 favonoids, and 8 various compounds, such as hydroxycoumarin, chalcone, benzofuran, benzodioxole, benzaldehyde, aromatic ketone, and alkyl cafeate ester. The established method demonstrates robust reliability and reproducibility, making it suitable for various applications including compositional identification, quantification, and quality assessment of the pharmacologically active constituents in Jianxin granules.