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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 (R2 = 0.801), while PV was lower (R2 = 0.469). However, the auto-prediction showed good agreement among the observed and predicted values in both PV (R2 = 0.802) and TBARS (R2 = 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.

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The analysis of soil organic matter (OM), total carbon (TC), and total nitrogen (TN) using traditional methods is quite time-consuming and involves the use of hazardous chemical reagents. Absorbance spectroscopy, especially near-infrared (NIR), is becoming more popular for soil analysis. This method requires little sample preparation, no chemicals, and a single spectral analysis to evaluate soil properties. Thus, this research aimed to develop an NIR spectroscopy method for the analysis of OM, TC, and TN in agricultural soils. These findings can provide a good concept of using PLS regression with NIR techniques. The method is as follows:•Topsoil (0-20 cm) samples were collected from various agricultural fields. OM, TC, and TN were analyzed using traditional methods and NIR spectroscopy.•NIR spectra were obtained using an FT-NIR spectrometer, original spectral including with Savitzky-Golay smoothing, standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing method were used to create a predicted model through Partial Least Squares (PLS) regression with 65 % calibration, and the rest 35 % for validation.•The results showed significant relationships between measured soil properties (SOM and TC) and NIR absorbance spectra in agricultural soil (R 2 of calibration and validation higher than 0.80).

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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.

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BACKGROUND: Mineral elements are nutrients required by an organism to perform functions necessary for survival. Stress-induced metabolism following nutritional stress has been reported to increase levels of anthocyanin. However, the role of mineral elements commonly found in soil and their contribution to the accumulation of anthocyanin content in rice plants is uncertain. RESULT: Amongst the ten mineral elements investigated, the cultivation of rice plants in clean sand showed that the Mg-, Se-, and Cu-treated plants had the highest accumulated anthocyanin content in the leaves, whereas B, Cr, and Se had the greatest effect on grains. Yield component data showed major positive effects from Mg, Cr, and B. The interaction of Zn*Se and Mg*Cu positively affected the anthocyanin content in grains. The self-organizing map indicated that the total anthocyanin content was relatively proportional to the concentration of Mn, B, and Cr, but disproportional to that of Se. However, rice plants with added Fe produced the smallest amount of total anthocyanin content, less than the control, in the four stages of rice growth. CONCLUSION: The appropriate concentrations of mineral elements in soil could promote the proliferation of anthocyanin content in rice plants and grains. © 2023 Society of Chemical Industry.

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This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, including corn, red beans, mung beans, black beans, soybeans, green and roasted coffee, adzuki beans, and paddy and white rice. These databases were established using a reference NIR instrument and the piecewise direct standardization (PDS) calibration transfer method. To evaluate the suitability of the transfer samples, the Davies-Bouldin index (DBI) was calculated. The outcomes that resulted in low DBI values were likely to produce better classification rates. The classification of coffee origins was based on the use of a supervised self-organizing map (SSOM). Without the spectral modification, SSOM classification using the developed NIR instrument resulted in predictive ability (% PA), model stability (% MS), and correctly classified instances (% CC) values of 61%, 58%, and 64%, respectively. After the transformation process was completed with the corn, red bean, mung bean, white rice, and green coffee NIR spectral data, the predictive performance of the SSOM models was found to have improved (67-79% CC). The best classification performance was observed with the use of corn, producing improved % PA, % MS, and % CC values at 71%, 67%, and 79%, respectively.

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Milk tablets are a popular dairy product in many Asian countries. This research aimed to develop an instant and rapid method for determining sucrose and lactose contents in milk tablets using near-infrared (NIR) spectroscopy. For the quantitative analysis, a training set composed of laboratory-scale milk samples was generated based on a central composite design (CCD) and used to establish partial least squares (PLS) regression for the predictions of sucrose and lactose contents resulting in R2 values of 0.9749 and 0.9987 with the corresponding root mean square error of calibration (RMSEC) values of 1.69 and 0.35. However, the physical difference between the laboratory-scale powder and the final product milk tablet samples resulted in spectral deviations that dramatically affected the predictive performance of the PLS models. Therefore, calibration transfer methods called direct standardization (DS) and piecewise direct standardization (PDS) were used to adjust the NIR spectra from the real milk tablet samples before the quantitative prediction. Using high-performance liquid chromatography (HPLC) as a reference method, the developed NIR-chemometric model could be used to instantly predict the sugar contents in real milk tablets by producing root mean square error of prediction (RMSEP) values for sucrose and lactose of 5.04 and 4.22 with Q2 values of 0.7973 and 0.9411, respectively, after the PDS transformation.

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Mango peel, a byproduct from the mango processing industry, is a potential source of food-grade mango peel pectin (MPP). Nonetheless, the influence of fruit physical characteristics and phytochemicals of peels on their correspondent pectin level has never been examined, particularly when high-quality food additives are of commercial need. Subsequently, the ultimate aim of the present study was to comprehend their relationship using chemometric data analyses as part of raw material sourcing criteria. Principal component analysis (PCA) advised that mangoes of 'mahachanok' and 'nam dok mai' could be distinguished from 'chok anan' and 'kaew' on the basis of physiology, peel morphology, and phytochemical characteristics. Only pectin extracted from mango var. 'chok anan' was classified as low-methoxyl type (Mox value ~4%). Using the partial least-squares (PLS) regression, the multivariate correlation between the fruit and peel properties and the degree of esterification (DE) value was reported at R2 > 0.9 and Q2 > 0.8. The coefficient factors illustrated that yields of byproducts such as seed and total biomass negatively influenced DE values, while they were positively correlated with crude fiber and xylose contents of the peels. Overall, it is interesting to highlight that, regardless of the differences in fruit varieties, the amount of biomass and peel proximate properties can be proficiently applied to establish classification of desirable properties of the industrial MPP.

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The purpose of this study is to access the existing awareness of nearly forgotten Thai detergent plants by the use of chemometrics tool. A Northern Thai forest dependent community was chosen as it played vital role on knowledge retaining of plant utilisations. For initial perception, ethnobotanical survey was conducted to determine usage of plants by the community. Then the utilised plant parts were screened for phytochemicals and their relationships with the defined cleansing terms (viz., shampoo, scrub, detergent, soap, scent and spiritual) were analysed by Principal Component Analysis (PCA). From the results, the most cited plants as known, used and found were Acacia concinna, Clitoria ternetea, Oryza sativa and Citrus hystrix. Biometric analyses advised that knowledge of detergent plant utilisation was well preserved at all age ranges and it was not variable with genders. Cluster analysis described that term 'spiritual' was not narrated with cleansing properties. For phytochemical analysis, plant extracts showed positive variable of bioactive ingredients and the main compounds in the extracts was saponins. These findings confirmed that the knowledge of indigenous plant utilisation was reserved by the forest dependent community and the information is beneficial toward local plant conservation movement.

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Cold-pressed oil is one of the healthiest plant extracts, but its use is limited only in some kinds of plants. Therefore, we aimed to investigate some potential cold-pressed oils with attractive fatty acid profiles and high amounts of functional phytochemicals. Fifty cold-pressed plant oils were prepared from various plant materials in Thailand, in which some of them were from uncommon or unattended plant materials. The oils included were nut oils (n = 9), pseudo-cereal oils (n = 9), legume oils (n = 3), amaranth oils (n = 3), marrow seed oils (n = 8), cruciferous seed oils (n = 7), and leafy green seed oils (n = 11). Gas-chromatography mass-spectrometry (GC-MS) and high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD) were employed to analyze fatty acid profile and five functional phytochemicals (e.g., phytosterols, cholecalciferol, and squalene). Saturated fatty acids were detected around 7.87-36.04%, monounsaturated fatty acids 10.17-80.25%, and polyunsaturated fatty acids nondetectable (ND)-78.25%, phytosterols 663-15123 µg g-1, squalene 265-5979 µg g-1, and cholecalciferol ND-1287.75 µg g-1. The study showed chemical characteristic of the analyzed oils: some contained good fatty acid composition and some were rich in functional phytochemical content. Among the obtained oils, marrow seed oils are a good source of phytosterol, cholecalciferol, and linoleic acid. Pseudo-cereal oils are rich in squalene and linolenic acid. Legume oils are rich in phytosterols and oleic acid. Besides, principal component analysis (PCA) was applied to identify the significance of oils that share compositional similarity (e.g., the samples from pseudo-cereal oil were found on the lower side of the PCA space, which separated them from marrow and leafy green seed oils distributed on the upper part of the plot). In summary, the qualitative and quantitative data would provide a good foundation for further application or selection of those plant oils for health purposes.

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The seeds of dried longan, one of the major processed fruits in Thailand, contain several bioactive compounds. In this study, we developed longan wine by incorporating its seeds during juice preparation and evaluated the antioxidant activities and volatile compounds in different conditions. The results suggested that Saccharomyces cerevisiae EC-1118 was suitable for fermentation of longan juice supplemented with 50% seed and 20% initial soluble solids at an optimal temperature of 30 °C. Different yeast strains showed various extents of antioxidant activities; however, the fermentation temperature and initial soluble solids of longan juice had little effect on the inhibition of reactive species. Antioxidant activities were significantly increased with increasing seed content. Dominant volatile compounds, which were independent of the winemaking conditions, were found to be phenethyl alcohol, 2,3-butylene glycol, 5-hydroxymethyl-2-furaldehyde, ethyl hydrogen succinate, and 4-hydroxyphenethyl alcohol. These compounds highly influenced the antioxidant activities of longan wine produced by incorporating the seeds.

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Frailty, one of the major public health problems in the elderly, can result from multiple etiologic factors including biological and physical changes in the body which contribute to the reduction in the function of multiple bodily systems. A diagnosis of frailty can be reached using a variety of frailty assessment tools. In this study, general characteristics and health data were assessed using modified versions of Fried's Frailty Phenotype (mFFP) and the Frail Non-Disabled (FiND) questionnaire (mFiND) to construct a Self-Organizing Map (SOM). Trained data, composed of the component planes of each variable, were visualized using 2-dimentional hexagonal grid maps. The relationship between the variables and the final SOM was then investigated. The SOM model using the modified FiND questionnaire showed a correct classification rate (%CC) of about 66% rather than the model responded to mFFP models. The SOM Discrimination Index (SOMDI) identified cataracts/glaucoma, age, sex, stroke, polypharmacy, gout, and sufficiency of income, in that order, as the top frailty-associated factors. The SOM model, based on the mFiND questionnaire frailty assessment, is an appropriate tool for assessment of frailty in the Thai elderly. Cataracts/glaucoma, stroke, polypharmacy, and gout are all modifiable early prediction factors of frailty in the Thai elderly.

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Sequential Extraction Procedure (SEP) can be used to evaluate the toxicity characteristics of heavy metals in soil, including arsenic (As), by separating the metals into several different fractions using selective extraction solvents in sequence. To accomplish this separation task, various factors that are known to affect the extraction process should be carefully considered. This research aimed to investigate the effect of the operational conditions on the SEP for As in soil using experimental designs. In the first step, a Plackett-Burman design was used twice to screen the important extraction variables from a total of 19 studied variables. As a result, SSR, extraction time of the first fraction (F1), and concentrations of sodium acetate (NaOAc) in F2 and ammonium oxalate (NH4Ox) in F6 were identified as significant to the amount of the extracted As. The selected variables were further investigated using a central composite design with response surface methodology. The optimized SEP characterized by 1:75 g:mL of SSR, an extraction time of 7 h 20 min of F1, 0.16 M of NaOAc and 0.11 M of NH4OAc were applied to extract a sample from contaminated agricultural soil obtained from the north of Thailand. The fractionation result was compared with the result obtained from a previously reported SEP method. It was found that similar extraction results could be achieved (91-97% As recovery). However, the optimized method revealed certain advantages in that it required dramatically less operation time (from 68 h to 32 h) and lower concentrations of the extraction solvents.

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Development of a highly accurate prediction model for protein-ligand inhibition has been a major challenge in drug discovery. Herein, we describe a novel predictive model for the inhibition of HIV-1 integrase (IN)-LEDGF/p75 protein-protein interaction. The model was constructed using energy parameters approximated from molecular dynamics (MD) simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations. Chemometric analysis using partial least squares (PLS) regression revealed that solvent accessible surface area energy (ΔGSASA) is the major determinant parameter contributing greatly to the prediction accuracy. PLS prediction model on the ΔGSASA values collected from 41 complexes yielded a strong correlation between the predicted and the actual inhibitory activities (R2 = 0.9666, RMSEC of pIC50 values = 0.0890). Additionally, for the test set of 14 complexes, the model performed satisfactorily with very low pIC50 errors (Q2 = 0.5168, RMSEP = 0.3325). A strong correlation between the buried surface areas on the IN protein, when bound with IN-LEDGF/p75 inhibitors, and the respective ΔGSASA values was also obtained. Furthermore, the current method could identify 'hot spots'of amino acid residues highly influential to the inhibitory activity prediction. This could present fruitful implications in binding site determination and future inhibitor developments targeting protein-protein interactions.Communicated by Ramaswamy H. Sarma.

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Essential oils from the aerial parts of four Elsholtzia species; Elsholtzia stachyodes, Elsholtzia communis, Elsholtzia griffithii and Elsholtzia beddomei were obtained by steam distillation and their chemical components were analysed by gas chromatography-mass spectrometry (GC-MS). Principle Component Analysis was used to identify the chemical variations in the essential oils from these plants, which could be categorised into two groups according to their main chemical components which are acylfuran derivatives and oxygenated monoterpenes. Additionally, the anti-acne inducing bacterial activity against Staphylococcus aureus and Staphylococcus epidermidis were evaluated. The oil from E. stachyodes was the most efficacious against the growth of S. aureus and S. epidermidis having MIC values of 0.78 and 1.56 µL/mL, respectively, and exhibited five times more effective than erythromycin (standard antibiotic).

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Improving performance of scoring functions for drug docking simulations is a challenging task in the modern discovery pipeline. Among various ways to enhance the efficiency of scoring function, tuning of energetic component approach is an attractive option that provides better predictions. Herein we present the first development of rapid and simple tuning models for predicting and scoring inhibitory activity of investigated ligands docked into catalytic core domain structures of HIV-1 integrase (IN) enzyme. We developed the models using all energetic terms obtained from flexible ligand-rigid receptor dockings by AutoDock4, followed by a data analysis using either partial least squares (PLS) or self-organizing maps (SOMs). The models were established using 66 and 64 ligands of mercaptobenzenesulfonamides for the PLS-based and the SOMs-based inhibitory activity predictions, respectively. The models were then evaluated for their predictability quality using closely related test compounds, as well as five different unrelated inhibitor test sets. Weighting constants for each energy term were also optimized, thus customizing the scoring function for this specific target protein. Root-mean-square error (RMSE) values between the predicted and the experimental inhibitory activities were determined to be <1 (i.e. within a magnitude of a single log scale of actual IC50 values). Hence, we propose that, as a pre-functional assay screening step, AutoDock4 docking in combination with these subsequent rapid weighted energy tuning methods via PLS and SOMs analyses is a viable approach to predict the potential inhibitory activity and to discriminate among small drug-like molecules to target a specific protein of interest.

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Self organizing maps (SOMs) in a supervised mode were applied for prediction of liquid chromatographic retention behavior of chemical compounds based on their quantum chemical information. The proposed algorithm was simple and required only a small alteration of the standard SOM algorithm. The application was illustrated by the prediction of the retention indices of bifunctionally substituted N-benzylideneanilines (NBA) and the prediction of the retention factors of some pesticides. Although the predictive ability of the supervised SOM could not be significantly greater than that of some previously established neural network methods, such as a radial basis function (RBF) neural network and a back-propagation artificial neural network (ANN), the main advantage of the proposed method was the ability to reveal non-linear structure of the model. The complex relationships between samples could be visualized using U-matrix and the influence of each variable on the predictive model could be investigated using component planes-which can provide chemical insight.

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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.

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A continuous process is monitored by on-line HPLC in four separate campaigns, ranging in duration from 10 to 104 h. Methods are reported that allow the study of variation over all four campaigns using Multilevel Simultaneous Components Analysis, which separates out the within- and between-campaign variation. In order to obtain control charts, Q- and D-statistics are combined with a within-campaign submodel (Simultaneous Components Analysis) to obtain a single model that is based only on within-campaign variation over all four campaigns.

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A continuous process was studied over 83.32 h using on-line high-performance liquid chromatography, involving the acquisition of 252 chromatograms. A method for analysis of these data using multivariate statistical process control on peak tables, in real-time, is described. The normal operating condition (NOC) region of the process was identified using evolving principal components analysis to be between 5.77 and 8.13 h. 19 out of the 37 peaks detected throughout the process were found in the NOC region, the remainder representing undesirable contaminants found elsewhere in the process. A major challenge is to develop the peak table as the process evolves, which is dynamically updated as new peaks are detected after the NOC region: this approach involving an "unlocked" peak table is contrasted to an approach using a "locked" peak table where only peaks detected during the NOC region are included in the model. In addition, results are compared to those obtained using baseline corrected and aligned chromatograms, using a NOC region of 5.85-8.33h. D- and Q-charts were obtained. It is shown that the "unlocked" peak table detects out of control samples best and provides good diagnostic insight into problems with the process.

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