Application of multivariate data analysis for food quality investigations: An example-based review

Physicochemical Parameters and Multivariate Analysis to Predict the Sensory Quality in Specialty Coffee from Panama

This study assessed the effectiveness of various multivariate calibration models in predicting the sensory evaluation scores of specialty coffee produced in Panama. The predictions were based on seven key physicochemical parameters of the beverage, considering the processing method used (natural or washed). To construct the models, three algorithms, Multiple Linear Regression (MLR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR), were employed, analyzing data sets for natural, washed, and combined processing methods. Model quality was evaluated using metrics such as the coefficient of determination (R2), root-mean-square error (RMSE) for cross-validation and prediction, and the residual predictive deviation (RPD). Among the physicochemical parameters, titratable acidity, soluble solids, and protein content showed a positive correlation with sensory scores, whereas pH exhibited an inverse relationship. The best-performing MLR and PCR models were those for the natural process, achieving R2p, RMSEp, and RPD values of 0.8293, 0.4239, and 2.34 for MLR and 0.7233, 0.5322, and 1.86 for PCR, respectively. Across all algorithms, models built exclusively with data from a single processing method consistently outperformed those that combined samples from both processes. PLSR models further demonstrated this trend, with R2p values of 0.7639 and 0.8306, RMSEp of 0.6891 and 0.3948, and RPD values of 2.07 and 2.51 for the washed and natural processes, respectively. In conclusion, the study highlights the critical importance of considering processing methods when developing multivariate models to predict the sensory evaluation scores of specialty coffee. Models built with samples from a uniform processing method yielded significantly better performance than those developed using mixed-process data sets.

Nondestructive Determination of Tocopherol and Tocotrienol in Vitamin E Powder Using Near- and Mid-Infrared Spectroscopy

Vitamin E is an essential nutrient, but its poor water solubility limits food and pharmaceutical applications. The usability of vitamin E can be enhanced via modification methods such as encapsulation, which transforms the physical state of vitamin E from a liquid to a powder. This study examined the efficacy of near-infrared (NIR) and mid-infrared (MIR) spectroscopy in identifying and predicting various vitamin E derivatives in vitamin E-encapsulated powder (VEP). An MIR analysis revealed the fundamental C-H vibrations of vitamin E in the range of 2700-3250 cm-1, whereas an NIR analysis provided information about the corresponding combination, first, and second overtones in the range of 4000-9000 cm-1. The MIR and NIR data were analyzed using a principal component analysis to characterize the VEP. Partial least squares (PLS) regression was applied to predict the content of individual vitamin E derivatives. PLS cross-validation revealed that NIR analysis provides more reliable predictive accuracy and precision for the contents of vitamin E derivatives, achieving a higher coefficient of determination for prediction (Q2) (0.92-0.99) than MIR analysis (0.20-0.85). For test set validation, the NIR predictions exhibited a significant level of accuracy, as indicated by a high ratio of prediction to deviation (RPD) and Q2. Furthermore, the PLS models developed using the NIR data had statistically significant predictive performance, with a high RPD (1.54-3.92) and Q2 (0.66-0.94). Thus, NIR spectroscopy is a valuable nondestructive technique for analyzing vitamin E samples, while MIR spectroscopy serves as a useful method for confirming its presence.

How do pretreatment and frozen storage impact the volatile profiles of Brussels sprouts and leek?

The volatile profiles of Brussels sprouts and leek, as affected by pretreatment combined with frozen storage were analyzed in the present work. The data revealed that, notwithstanding the effect upon pretreatment seemed to be major compared to the effect upon frozen storage, the latter was existent. Pretreatment yielded volatile compounds that could be associated with (bio)chemical reaction pathways in both vegetables. For frozen storage at -20 °C, the effect for leek appeared to be the largest for the blanched and raw samples, possibly due to a substantial amount of substrates present when frozen storage was initiated in this sample compared to the other samples. Those substrates were apparently more prone to be affected upon frozen storage. For Brussels sprouts, this observation was less outspoken. Remarkably, the abundance of markers in pretreated Brussels sprouts seemed to show a decreasing linear trend towards the end of the frozen storage period at -20 °C. As industrial relevant conditions were considered and compared, the insights gained in this study might be relevant to implement on industrial level.

Physical properties and mass models of Deglet Noor and Arichti semi-dry Algerian date fruits: A comparative study

For thousands of years, date fruit (Phoenix dactylifera L.) has been a popular diet in arid and semi-arid locations. It has religious importance for Muslims and is especially important during the holy month of Ramadan. The global output of date fruits has been continuously expanding, with Arab nations accounting for a sizable portion. The emphasis of this research is on two popular semi-dry Algerian date fruit types, Deglet Noor and Arechti, which are grown in the Ziban region. These fruits' physical parameters, such as size, sphericity, surface area, volumes, and density, were determined. The goal of this study was to create a mass model based on these physical attributes to help in the grading and sorting of date fruits. Fruit mass was shown to be closely connected to linear dimensions, arithmetic and geometric mean diameters, surface area, and volumes. Correlations between mass and physical attributes were established using a variety of mathematical models, including linear, quadratic, S-curve, and power models. The results demonstrated the applicability of specific factors for mass modeling, offering useful insights for the development of system sizing and conservation. With good correlation, multivariate data analysis was employed to correctly estimate the mass of both kinds. This research advances our understanding of the physical features of Algerian date fruits and their connection to mass, allowing for better handling, sorting, and packing processes in the worldwide date market.

Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Correlation of the presence of acrolein with higher alcohols, glycerol, and acidity in cachaças

Acrolein is a toxic aldehyde that can be present in various beverages, such as cachaça and other distilled spirits from sugarcane. The objective of this work was to detect and quantify acrolein in samples of cachaça produced by different processes in all regions of Brazil and to evaluate the possible routes of formation of this contaminant from the correlation with other secondary compounds present in the beverage using principal component analysis. Approximately 27.0% of the samples analyzed were outside the limit established by Brazilian legislation for this contaminant, with an average acrolein concentration of 14.01 mg 100 mL^-1 anhydrous alcohol (aa). In the other samples, the average concentration was 0.97 mg 100 mL^-1 aa. After selecting the variables that most closely correlated with the presence of acrolein in beverages, a positive correlation was found with the presence of butan-2-ol, propan-1-ol and volatile acids, and a slight correlation with the presence of phenolic compounds. Therefore, the presence of acrolein in cachaça can be associated with contamination of the fermentation must by bacteria of the genus Lactobacillus, as a result of the chemical degradation and enzymatic conversion of the glycerol produced during fermentation.

Analysis of the water state and microfractal dimension of tilapia fillets during freezing and thawing

To study the effects of freezing and thawing times and freezing temperatures on the water state and microstructure of tilapia fillets, experiments on tilapia fillets were carried out at -4 and -18°C with one to four freezing and thawing cycles (FTCs). Low-field nuclear magnetic resonance (LF-NMR) and nuclear magnetic resonance imaging were used to observe the water state after different treatments, and scanning electron microscopy (SEM) and frozen sections were used to observe the microstructure changes. Fractal dimension (FD) was used to quantitatively characterize the microstructure of the fish tissue, and the correlation between FD and fish fillet quality parameters was studied by principal component analysis (PCA). The findings showed that with the increase of FTCs, the thawing loss increased, and the water holding capacity (WHC) fell. FTCs cause a decrease in immobilized water and an increase in free water in the fillet. This indicates the migration of immobilized water to free water. SEM and frozen slice images showed that the growth of ice crystals led to the destruction of myogenic fibers. A decrease in freezing temperature inhibited ice crystal growth. The FD value dropped in accordance with an increase in FTCs. PCA demonstrated that the WHC, NMR data, and FD value had a strong correlation with the quality changes in the tilapia fillets. Therefore, FD and water state can reflect the quality characteristics of tilapia fillets. PRACTICAL APPLICATION: The water migration in tilapia fillets is detected with LF-NMR, and the microscopic image may be quantified using the FD value. Both approaches can offer fresh perspectives on how to assess the quality of tilapia fillets and reflect changes in their quality.

The Characterization of Dry Fermented Sausages under the "Chorizo Zamorano" Quality Label: The Application of an Alternative Statistical Approach

The characterization of quality brand meat products, such as "Chorizo Zamorano" dry fermented sausages, involves a wide range of data which makes it necessary to use alternative statistical methodologies. In this study, the feasibility of the Categorical Principal Components Analysis as a multivariate non-linear technique for the characterization of "Chorizo Zamorano" was assessed. The data analyzed were those of eight commercial brands covered by the quality mark over an eight-year period (2013-2020) and included parameters of the physicochemical composition and organoleptic properties of the product. The results showed that "Chorizo Zamorano" has an average moisture content (28.28%), high protein (38.38%) and fat (51.05%) contents, and a very low carbohydrate concentration (1.52%). Results showed that the fat and protein content and the sensory parameters related to external and internal odor appeared to be the studied variables with the greatest influence on the classification of the products according to their quality.

Design of biomass-based composite photocatalysts for wastewater treatment: a review over the past decade and future prospects

This investigation applied a systematic review approach on publications covering primary data during 2012-2022 with a focus on photocatalytic degradation of pollutants in aqueous solution by composite materials synthesized with biomass and, at least, TiO₂ and/or ZnO semiconductors to form biomass-based composite photocatalysts (BCPs). After applying a set of eligibility criteria, 107 studies including 832 observations/entries were analyzed. The average removal efficiency and degradation kinetic rate reported for all model pollutants and BCPs were 77.5 ± 21.5% and 0.064 ± 0.174 min^-1, respectively. Principal component analysis (PCA) was applied to analyze BCPs synthesis methods, experimental conditions, and BCPs' characteristics correlated with the removal efficiency and photodegradation kinetics. The relevance of adsorption processes on the pollutants' removal efficiency was highlighted by PCA applied to all categories of pollutants (PCA_pol). The PCA applied to textile dyes (PCA_dyes) and pharmaceutical compounds (PCA_pharma) also indicate the influence of variables related to the composite synthesis (i.e., thermal treatment and time spent on BCPs synthesis) and photocatalytic experimental parameters (catalyst concentration, pollutant concentration, and irradiation time) on the degradation kinetic accomplished by BCPs. Furthermore, the multivariate analysis (PCA_pol) revealed that the specific surface area and the narrow band gap are key characteristics for BCPs to serve as a competitive photocatalyst. The effect of scavengers on pollutants' degradation and the recyclability of BCPs are also discussed, as necessary aspects for scalability trends. Further investigations are recommended to compare the performance of BCPs and commercial catalysts, as well as to assess the costs to treat real wastewater.

A Coding Basis and Three-in-One Integrated Data Visualization Method 'Ana' for the Rapid Analysis of Multidimensional Omics Dataset

With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB® coding basis and three-in-one integrated method, 'Ana', was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research.

Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications

Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications.