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Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

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

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Roasting is essential for developing the characteristic aroma of flaxseed oil (FSO), yet its impact on oil quality remains underexplored. This study employed headspace-gas chromatography-mass spectrometry coupled with multivariate analysis to elucidate the dynamic changes in volatile compounds and quality characteristics of FSO subjected to varying roasting temperatures. Our findings revealed that seven key aroma compounds, identified through the variable importance in the projection scores of partial least square-discrimination analysis models and relative aroma activity value, served as molecular markers indicative of distinct roasting temperatures. These compounds included 2,5-dimethylpyrazine, 2-pentylfuran, (E)-2-pentenal, 2-ethyl-3,6-dimethylpyrazine, heptanal, octanal, and 2-hexenal. Notably, roasting at 200°C was found to enhance oil stability and antioxidant capacity, with phenolic compounds and Maillard reaction products playing synergistic roles in bolstering these qualities. Network analysis further uncovered significant correlations between these key aroma compounds and quality characteristics, offering novel perspectives for assessing FSO quality under diverse roasting conditions. This research not only enriched our understanding of the roasting process's impact on FSO but also provided valuable guidance for the optimization of industrial roasting practices. This study would provide important practical applications in aroma regulation and process optimization of flaxseed oil. .

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Quality of pet foods can be affected by many factors such as raw materials, formulations, and processing techniques. The pet food manufacturers require fast analyses to control the nutritional quality of their products. Herein, near-infrared spectroscopy (NIR) was evaluated to quantify the chemical composition of pet food, and the performances of two NIR spectrometers were investigated and compared: a benchtop instrument (1000-2500 nm) and a low-cost handheld instrument (900-1700 nm). Seventy cat food and thirty-six dog samples were characterized using reference methods for crude protein, crude fat, crude fibre, crude ash, moisture, calcium (Ca), and phosphorus (P). Principal component regression (PCR) and partial least squares regression (PLSR) were used to establish the models that involved the cat food and mixed model. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The Optimal models obtained from the benchtop instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (Residual prediction variation (RPD) > 3), for crude fibre were classified as "Poor" (RPD>2), and for crude ash, Ca and P (RPD<2) were classified as "Very poor". The Optimal calibrations obtained from the handheld instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (RPD>3), for crude fibre, crude ash, Ca, and P were classified as "Very poor" (RPD<2). Generally, the the performance of benchtop and handheld instrument was close, and the cat food model outperformed the mixed model. Results from the current study revealed the potential to monitor the chemical compositions in pet food on a large scale.

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Introduction: Claw lesions significantly contribute to lameness, greatly affecting sow welfare. This study investigated different factors that would impact the severity of claw lesions in the sows of Brazilian commercial herds. Methods: A total of 129 herds (n = 12,364 sows) were included in the study. Herds were in the Midwest, Southeast, or South regions of Brazil. Inventory sizes were stratified into 250-810 sows, 811-1,300 sows, 1,301-3,000 sows, and 3,001-10,000 sows. Herds belonged to Cooperative (Coop), Integrator, or Independent structures. The herd management was conducted either maintaining breeds from stock on-site (internal), or through purchase of commercially available genetics (external). Herds adopted either individual crates or group housing during gestation. Within each farm, one randomly selected group of sows was scored by the same evaluator (two independent experts evaluated a total of 129 herds) from 0 (none) to 3 (severe) for heel overgrowth and erosion (HOE), heel-sole crack (HSC), separation along the white line (WL), horizontal (CHW) and vertical (CVW) wall cracks, and overgrown toes (T), or dewclaws (DC) in the hind legs after parturition. The study assessed differences and similarities between herds using Principal Component Analysis (PCA) and Hierarchical Agglomerative Clustering (HAC) analysis. The effects of factors (i.e., production structure, management, housing during gestation, and region) were assessed using the partial least squares method (PLS). Results and discussion: Heel overgrowth and erosion had the highest prevalence, followed by WL and CHW, while the lowest scores were observed for T, DC, and CVW. Herds were grouped in three clusters (i.e., C1, C2, and C3). Heel overgrowth and erosion, HSC, WL, CHW, CVW, and T were decreased by 17, 25, 11, 25, 21, and 17%, respectively, in C3 compared to C1 and 2 combined. Independent structure increased the L-Index in all three clusters. Furthermore, individual housing increased the L-Index regardless of the cluster. The results suggest that shifting toward larger, more technologically advanced herds could potentially benefit claw health. Additionally, adopting group gestation housing appears to mitigate the adverse effects on claw health, although further validation is necessary, as Brazil has only recently transitioned from individual housing practices.

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Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

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Erythrogram, despite its prevalent use in assessing red blood cell (RBC) disorders and can be utilized to evaluate various diseases, still lacks evidence supporting the effects of per- and polyfluoroalkyl substances (PFASs) and organophosphate esters (OPEs) on it. A cross-sectional study involving 467 adults from Shijiazhuang, China was conducted to assess the associations between 12 PFASs and 11 OPEs and the erythrogram (8 indicators related to RBC). Three models, including multiple linear regression (MLR), sparse partial least squares regression, and Bayesian kernel machine regression (BKMR) were employed to evaluate both the individual and joint effects of PFASs and OPEs on the erythrogram. Perfluorohexane sulfonic acid (PFHxS) showed the strongest association with HGB (3.68%, 95% CI: 2.29%, 5.10%) when doubling among PFASs in MLR models. BKMR indicated that PFASs were more strongly associated with the erythrogram than OPEs, as evidenced by higher group posterior inclusion probabilities (PIPs) for PFASs. Within hemoglobin and hematocrit, PFHxS emerged as the most significant component (conditional PIP = 1.0 for both). Collectively, our study emphasizes the joint effect of PFASs and OPEs on the erythrogram and identified PFASs, particularly PFHxS, as the pivotal contributors to the erythrogram. Nonetheless, further investigations are warranted to elucidate the underlying mechanisms.

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This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

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Blood is commonly discovered at crime scenes in various forms, including stains, dried residue, pools, and fingerprints on assorted surfaces. Estimating the age of bloodstains is a crucial aspect of reconstructing crime scenes. This research aimed to investigate how the nature of different surfaces affects the estimation of bloodstain age, utilizing a reliable and non-destructive approach. The study employed ATR-FTIR spectroscopy in conjunction with Chemometric techniques such as PCA (Principal Component Analysis) and OPLSR (Orthogonal Signal Correction Partial Least Square Regression Analysis) to analyze spectral data and develop regression models for estimating bloodstain age on cement, metal, and wooden surfaces for up to eleven days. The chemometric models for bloodstains on all three substrates demonstrated strong performance, with predictive Root Mean Square Error (RMSE) values ranging from 1.1 to 1.43 and R2 values from 0.84 to 0.89. Notably, the model developed for metal surfaces was found to be the most accurate with minimal prediction error. The findings of the study showed that the porosity of the substrates upon which bloodstains were found had a discernible influence on the age-related transformations observed in bloodstains; the majority of which occured within the spectral range of 2800 cm- 1 to 3500 cm- 1.

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Dushan shrimp sour paste (DSSP), a traditional Guizhou condiment, and its unique flavor is determined by the fermentation microbiota. However, the relationship between the microbiota structure and its flavor remains unclear. This study identified 116 volatile flavor compounds using electronic nose and headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GC-MS) techniques, of which 19 were considered as key flavor compounds, mainly consisting of 13 esters and 1 alcohol. High-throughput sequencing technique, the bacterial community structure of nine groups of DSSPs was determined. Further analysis revealed Vagococcus, Lactococcus, and Tepidimicrobium as key bacteria involved in flavor formation. This study contributes to our understanding of the relationship between bacterial communities and the flavor formation, and provides guidance for screening starter culture that enhance the flavor of DSSP in industrial production.

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The oyster mushroom is cultivated globally, renowned for its unique texture and umami flavor, as well as its rich content of nutrients and functional ingredients. This study aims to identify the descriptive sensory characteristics, assess the consumer acceptability of new superior lines and cultivars of yellow oyster mushrooms, in addition to exploring the relationship between these descriptive characteristics and consumer acceptability. Statistical analyses were performed using one-way analysis of variance (ANOVA), principal component analysis (PCA), and partial least squares regression (PLSR). Twenty attributes were delineated, including three related to appearance/color (gray, yellow, and white), four associated with the smell/odor of fresh mushroom (oyster mushroom, woody, fishy, and seafood smells), three pertaining to the smell/odor of cooked mushrooms (mushroom, umami, and savory smells), four describing flavor/taste (sweet, salty, umami, and savory tastes), and five for texture/mouthfeel (chewy, smooth, hard, squishy, and slippery textures). Consumer acceptability tests involved 100 consumers who evaluated overall liking, appearance, overall taste, sweetness, texture, savory taste, MSG taste, smell, color, purchase intention, and recommendation. The general oyster mushroom (548 samples) scored highest in acceptability. Seven attributes, namely fresh mushroom smell, seafood smell (fresh), fishy smell (fresh), umami smell (cooked), nutty smell (cooked), salty taste, and MSG taste with the exception of appearance showed significant differences among samples (p < 0.001). The three yellow oyster mushroom samples were strongly associated with attributes like hardness, softness (texture), sweet taste (745 samples), MSG taste, salty taste, squishy texture, and fishy smell (483 and 629 samples). The development of sensory lexicons and increasing consumer acceptance of new superior lines and cultivars of yellow oyster mushroom will likely enhance sensory quality and expand the consumer market, aligning with consumer needs and preferences.

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Rainfed regions have inconsistent spatial and temporal rainfall. So, these regions could face water deficiency during critical stages of crop growth. In this regard, multi-environment trials could play a key role in introducing stable genotypes with good performance across several rainfed regions. Grass pea, as a potential forage crop, is a resilient plant that could grow in unsuitable circumstances. In this study, agro-morphological attributes of 16 grass pea genotypes were examined in four semi-warm rain-fed regions during the years 2018-2021. The MLM analysis of variance showed a significant genotype-by-environment interaction (GEI) for dry yield, seed yield, days to maturity, days to flowering, and plant height of grass pea. The PLS (partial least squares) regression revealed that rainfall in the grass pea establishment stage (October and November) is meaningful. For grass pea cultivation, monthly rainfall during plant growth is important, especially in May, with an aim for seed yield. Regarding dry yield, G5, G10, G11, G12, G13, and G15 were selected as good performers and stable genotypes using DY × WAASB biplots, while SY × WAASB biplot manifested G2, G3, G12, and G13 as superior genotypes with stable seed yield. Considering equal weights for yield as well as the WAASB stability index (50/50), G13 was selected as the best one. Among test environments, E2 and E11 played a prominent role in distinguishing the above genotypes from other ones. In this study, MTSI (multi-trait stability index) analysis was applied to select a stable genotype, considering all measured agro-morphological traits simultaneously. Henceforth, the G5 and G15 grass pea genotypes were discerningly chosen due to their commendable performance in the WAASBY plot. In this context, G13 did not emerge as the winner based on MTSI; however, it exhibited an MTSI value in close proximity to the outer boundary of the circle. Consequently, upon comprehensive consideration of all traits, it is deduced that G5, G13, and G15 can be appraised as promising superior genotypes with stability across diverse environmental conditions.

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Fish oils are good sources for essential fatty acids such as omega-3 and omega-6 fatty acids needed to human growth. Indonesia is rich in fish species and among this, red snapper fish (Lutjanus sp.) can be extracted to get red snapper fish oils (RSFOs). The aim of this study was to classify and discriminate RSFO from different origins using Fourier-transform infrared (FTIR) spectra and pattern recognition techniques. All of the RSFO's FTIR spectra were very similar. The FTIR vibrations showed the presence of triglycerides as the main composition in fish oils. Principal component analysis (PCA) could separate the RSFO according to sample origin. Supervised pattern recognition of partial least square-discriminant analysis (PLS-DA) and sparse PLS-DA (sPLS-DA) successfully discriminated and classified different Lutjanus species of fish oils obtained from different origins. The vibration of functional groups at 1711, 1653, 1745, and 3012 per cm were considered for their important contributions in discriminating of Lutjanus species (variable importance in projection, variable importance in the projection score >1). Fish oils obtained from the same species were classified into the same class indicating similar chemical compositions. Among the three pattern recognition techniques used, sPLS-DA offers the best model for the discrimination and classification of Lutjanus fish oils. It can be concluded that FTIR spectroscopy in combination with the pattern recognition technique is the potential to be used for of fish oil authentication to verify the quality of the fish oils. It can be further developed as a rapid and effective method for fish oil authentication.

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The aim of this experiment was to investigate the effect of storage temperature and pH on phenolic compounds of Phyllanthus emblica juice. Juice was stored at different temperatures and pH for 15 days and sampled on 2-day intervals. The browning index (BI, ABS420 nm), pH, centrifugal precipitation rate (CPR), and phenolic compounds were evaluated. The results showed 4°C and pH 2.5 could effectively inhibit browning and slow down pH drop of P. emblica juice. The result of orthogonal partial least square-discriminant analysis showed P. emblica juice stored at 4°C and pH 2.5 still had a similar phenolic composition, but at 20°C, 37°C, and pH 3.5, the score plots were concentrated only in the first 3 days. Additionally, gallic acid (GA) and ellagic acid (EA) were screened out to be the differential compounds for browning of P. emblica juice. The contents of GA, epigallocatechin (EGC), corilagin (CL), gallocatechin gallate (GCG), chebulagic acid (CA), 1,2,3,4,6-O-galloyl-d-glucose (PGG), and EA were more stable at 4°C and pH 2.5. Overall, during storage at 4°C and pH 2.5, it could inhibit the increase of GA and EA and decrease of CL, GCG, CA, and PGG, whereas EGC did not show significant difference between storage conditions. The CPR was higher at 4°C, while pH 2.5 could reduce the CPR. In conclusion, in order to maintain stability of phenolic compounds and extended storage period, the P. emblica juice could be stored at low temperature and adjust the pH to increase the stability of juice system.

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Termites are social insects with high species diversity in tropical ecosystems. Multivariate analysis with near-infrared spectroscopy (NIRS) and data interpretation can separate social insects belonging to different colonies of the same species. The objective of this study was to propose the use of discriminant analysis by partial least squares (PLS-DA) combined with NIRS to identify the colonial origin of the Syntermes grandis (Rambur, 1842) (Blattodea: Termitidae) in 2 castes. Six ground S. grandis colonies were identified and mapped; 30 workers and 30 soldier termites in each colony were submitted to spectral measurement with NIRS. PLS-DA applied to the termites' spectral absorbance was used to detect a spectral pattern per S. grandis colony by caste. PLS-DA regression with NIRS proved to be an approach with 99.9% accuracy for identifying the colonial origin of S. grandis workers and 98.3% for soldiers. The methodology showed the importance of qualitatively characterizing the colonial phenotypic response of this species. NIRS is a high-precision approach to identifying the colony origin of S. grandis workers and soldiers. The PLS-DA can be used to design ecological field studies to identify colony territorial competition and foraging behavior of subterranean termite species.

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Polycyclic aromatic hydrocarbons (PAHs) present in oily sludge generated by the petroleum and petrochemical industries have emerged as a prominent concern within the realm of environmental conservation. The precise determination of PAHs holds immense significance in both petroleum geochemistry and environmental protection. In this study, a combination of surface-enhanced Raman spectroscopy (SERS) and solid-liquid extraction was employed for the screening of PAHs in oily sludge. Methanol was utilized as the extraction solvent for PAHs, while nanosilver-silicon coupling substrates were employed for their detection. The SERS spectrum was acquired using a portable Raman spectrometer. The nano silver-silicon coupling substrate exhibits excellent uniformity, with relative standard deviations (RSDs) of Phenanthrene, Fluoranthrene, Fluorene and Naphthalene (Phe, Flt, Flu and Nap) being 2.8%, 1.08%, 1.41%, and 5.44% respectively. Moreover, the limits of detection (LODs) achieved remarkable values of 0.542 µg/g, 0.342 µg/g, 0.541 µg/g, and 5.132 µg/g. The quantitative analysis of PAHs in oily sludge was investigated using SERS technology combined with partial least squares (PLS). The optimal PLS calibration model was optimized by combining spectral preprocessing methods and using the SiPLS (Synergy interval partial least squares)-VIP (Variable Importance in Projection) hybrid variable selection strategy. The prediction performance of the D1st (First derivative)-WT (Wavelet transform)-SiPLS-VIP-PLS model was deemed satisfactory, as evidenced by high R2P values of 0.9851, 0.9917, and 0.9925 for Phe, Flt, and Flu respectively; additionally, the corresponding MREP values were found to be 0.0580, 0.0668, and 0.0669 respectively. However, for Nap analysis, the D1st-WT-PLS model proved to be a better calibration model with an R2P value of 0.9864 and an MREP (Mean relative error of prediction) value of 0.0713. In summary, SERS technology combined with PLS based on different spectral pretreatment methods and mixed variable selection strategies is a promising method for quantitative analysis of PAHs in oily sludge, which will provide new ideas and methods for the quantitative analysis of PAHs in oily sludge.

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This study was designed to reveal the relationship among browning, polyphenol degradation, Maillard reaction (MR) and flavor variation in jujube fruit (JF) during air-impingement jet drying (AIJD). Five kinds of JFs were dried by AIJD at 60 °C and vacuum freeze drying. Colorimeter and chemometric analysis found that AIJD induced color changes of JF pulp and peel. AIJD also reduced the total polyphenols content and total flavonoids levels in JF. The Fe3+ reducing capacity and 2,2'-Azinobis-(3-ethylbenzothiazoline-6-sulphonate) cationic radical scavenging capacity of JF were reduced by 31.6% and 8.2%, respectively. Seven polyphenols were identified in JF, and epicatechin was found related to change of JF pulp color by sparse partial least square (sPLS). sPLS revealed that 3-deoxy glucosone, N-ε-carboxymethyl-l-lysine and 5-hydroxymethylfurfural associated with JF color. sPLS found that MR generated 3-methyl-butanoic acid and cyclobutanone during AIJD of JF. Chemometrics is an effective tool to disclose mechanism of color changes in food.

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INTRODUCTION: Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. OBJECTIVE: The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. METHODS: Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. RESULTS: The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. CONCLUSION: The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

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(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.

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Green analytical approaches are employed for the determination of active pharmaceutical ingredients, in conjunction with their impurities. Smart chemometric spectrophotometric techniques, including orthogonal partial least square (OPLS), variable selection such as genetic algorithm (GA-OPLS), and interval selection (i-OPLS), were utilized. These chemometric models were implemented for assessing six proton-pump inhibitors Omeprazole, Esomeprazole, Lansoprazole, Pantoprazole, Rabeprazole, and Dexlansoprazole along with two selected official impurities, namely 4-Desmethoxy omeprazole impurity and Rabeprazole-impurity B. Experimental design was implemented to separate impurities, in the process of multivariate calibration, a five-level eight-factor calibration design consisting of 25 samples was selected. This design was deliberately selected to guarantee that the components were mutually orthogonal to assess the model's performance and reliability, a separate validation set of 15 samples was constructed. The best-performing of the proposed techniques were identified by considering the least favorable values of the Correlation Coefficient (R ≥ 0.9995), the Root Mean Square Error of Prediction (RMSEP) values between (0.0102-0.5622), and the Relative Error of Prediction (REP) values between (0.2961-1.1917). The proposed and reported methods' greenness-sustainability was quantitatively evaluated, and a comparative study of the greenness profile was established through a spider chart, the National Environmental Method Index tool, advanced and modified NEMI along with the Hexagon tool, and the whiteness qualities of the presented approaches were assessed by implementing the recently adopted Red-Green-Blue paradigm and White Analytical Chemistry tool. These approaches are well-suited for use in quality control laboratories due to their observed acceptance, long-term sustainability, simplicity, and affordability.