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Summary: Maltodextrin is a crucial ingredient in food and pharmaceutical sectors. Traditional quality assessment methods for maltodextrin are destructive and time consuming. This study aimed to employ near‐infrared (NIR) spectroscopy and chemometrics to differentiate maltodextrin variants and measure their quality parameters efficiently. NIR spectra were recorded in the range of 12 000–4000 cm−1 using the transflectance mode. The classification model effectively distinguished maltodextrin types based on their dextrose equivalent (DE) values using techniques such as partial least squares‐discriminant analysis and supervised self‐organising map (SSOM). Moreover, quality parameters including moisture content, DE value, maltose, maltotriose, pH, and SO2 were quantitatively assessed using partial least squares regression (PLSR) and SSOM models. Particularly, PLSR provided better results, with residual predictive deviation values exceeding 2.5 for moisture content, DE values, maltose, and maltotriose. These models can be applied for use in both laboratory settings and industrial monitoring. [ABSTRACT FROM AUTHOR]

BACKGROUND: Industrial starch hydrolysis allows the production of syrups with varying functionality depending on their Brix value and dextrose equivalent (DE). As the current methods for evaluating these products are labor‐intensive and time‐consuming, the objective of this study was to investigate the potential of near‐infrared (NIR) spectroscopy for classifying the different tapioca starch hydrolysis products. RESULTS: NIR spectra of samples of seven products (n = 410) were recorded in transflectance mode in the 12 000–4000 cm−1 range. Next, orthogonal partial least squares (OPLS) regression models were built to predict the Brix and DE values of the different samples. To classify the different starch hydrolysis products, support vector machines (SVM) were trained using either the raw spectra or latent variables (LVs) obtained from the OPLS models. The best classification accuracy was obtained by the SVM classifier based on the LVs from the OPLS model for DE prediction, resulting in 95% correct classification over all classes. CONCLUSION: These results show the potential of NIR spectroscopy for classifying tapioca starch hydrolysis products with respect to their functional properties related to the Brix and DE values. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A green chemistry experiment is presented for teaching an undergraduate quantitative analysis/analytical chemistry laboratory class through the use of black tea leaf extract for the determination of iron in supplement tablets. Dried tea leaf is a safe, low cost, and widely available source of natural extract that can be used in place of more toxic chemicals (for example, 1,10-phenanthroline) or other uncommon natural reagents that may be hard to obtain outside their native lands. Various student learning outcomes accommodated within this experiment include the principles and hands-on operation of a spectrophotometer, the construction of a calibration graph and obtaining a linear regression equation using Microsoft Excel, the determination of figures of merit (limit of detection, limit of quantitation, and linear working range), and sampling and preparation of supplement tablets with acid digestion. Students were trained in quality control and quality assurance through statistical data analysis for precision (SD) and accuracy (t test, percent recovery, and percent error) for the experiment. They were asked to discuss not only advantages but also limitations of using natural reagents for chemical analysis. Critical thinking and a deep understanding of the experiment were assessed through the students' ability to answer follow-up questions in their report and on the exam. Class management and modification of the experiment to suit a laboratory with limited facilities are discussed. This robust tea-Fe experiment was successfully demonstrated for teaching both large and small laboratory classes at two institutions, one in the U.S. and one in Thailand.

Coffee bean oxidation is associated with enzymatic and non-enzymatic browning, the degradation of desirable aromatic compounds, the development of undesirable flavors, increased susceptibility to microbial spoilage, and volatile compound losses. This study investigated natural dry process (DP) and honey process (HP) green coffee beans stored in GrainPro® bags for 0, 5, 10, and 20 days under accelerated storage conditions at 30 °C, 40 °C, and 50 °C with relative humidity of 50%. A kinetic model was used to estimate the shelf life of the green coffee beans. DP recorded durability of 45.67, 29.9, and 24.92 days at 30 °C, 40 °C, and 50 °C, respectively, with HP 60.34, 38.07, and 19.22 days. Partial least squares (PLS) analysis was performed to build the models in order to predict the shelf life of coffee based on peroxide (PV) and thiobarbituric acid reactive substances (TBARS) values. In terms of prediction with leave-one-out cross-validation (LOOCV), PLS provided a higher accuracy for TBARS ( R 2 = 0.801), while PV was lower ( R 2 = 0.469). However, the auto-prediction showed good agreement among the observed and predicted values in both PV ( R 2 = 0.802) and TBARS ( R 2 = 0.932). Based on the variable importance of projection (VIP) scores, the ATR-FTIR peaks as 3000–2825, 2154–2150, 1780–1712, 1487–2483, 1186–1126, 1107–1097, and 1012–949 cm−1 were identified to be the most related to PV and TBARS on green coffee beans shelf life. ATR-FITR showed potential as a fast and accurate technique to evaluate the oxidation reaction that related to the loss of coffee quality during storage. [ABSTRACT FROM AUTHOR]

A new approach for process monitoring is described, the self-organizing map quality control (SOMQC) index. The basis of the method is that SOM maps are formed from normal operating condition (NOC) samples, using a leave-one-out approach. The distances (or dissimilarities) of the left out sample can be determined to all the units in the map, and the nth percentile measured distance of the left out sample is used to provide a null distribution of NOC distances which is generated using the Hodges--Lehmann method. The nth percentile distance of a test sample to a map generated from all NOC samples can be measured and compared to the null distribution at a given confidence level to determine whether the sample can be judged as out of control. The approach described in this paper is applied to online high-performance liquid chromatography (HPLC) measurements of a continuous pharmaceutical process and is compared to other established methods including Q and D statistics and support vector domain description. The SOMQC has advantages in that there is no requirement for multinormality in the NOC samples, or for linear models, or to perform principal components analysis (PCA) prior to the analysis with concomitant issues about choosing the number of PCs. It also provides information about which variables are important using component planes. The influence of extreme values in the background data set can also be tuned by choosing the distance percentile. 10.1021/ac100383g