Application of near-infrared spectroscopy/artificial neural network to quantify glycosylated norisoprenoids in Tannat grapes

Intelligent modelling of sugarcane juice quality characteristics based on microfluidization processing conditions

This investigation employed different ANN infrastructures for predicting the quality of sugarcane juice under varying microfluidization pressures (50-200 MPa) and cycles (1-7) which was previously unexplored. Two hidden layer (HL) activation functions (tansigmoid, logsigmoid) and learning algorithms (LM, GDX) with varying hidden layer neurons (HLNs) were tested to predict the color, total phenol content, total flavonoid content, chlorophyll content, total and reducing sugars, polyphenol oxidase activity, peroxidase activity, sucrose neutral invertase activity, aerobic plate count, yeast and mold count, particle size, sensory score and sedimentation rate of sugarcane juice under different microfluidization processing conditions. Results showed that the combination of LM + logsigmoid, GDX + logsigmoid and GDX + tansigmoid produced > 90% prediction accuracy. Among these models, GDX + tansigmoid exhibited 91.7% accuracy on training, and 96% accuracy on testing using relatively lower number of neurons (10 HLNs), and was therefore selected to predict the quality characteristics of sugarcane juice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-024-05994-2.

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Application of near-infrared spectroscopy for the estimation of volatile compounds in Tempranillo Blanco grape berries during ripening

BACKGROUND

The knowledge of volatile compounds concentration in grape berries is very valuable information for the winemaker, since these compounds are strongly involved in the final wine quality, and in consumer acceptance. In addition, it would allow to set the harvest date according to aromatic maturity, to classify grape berries according to their quality and to make wines with different characteristics, among other implications. However, so far, there are no tools that allow the volatile composition to be measured directly on intact berries, either in the vineyard or in the winery.

RESULTS

In this work, the use of near-infrared (NIR) spectroscopy to estimate the aromatic composition and total soluble solids (TSS) of Tempranillo Blanco grape berries during ripening was evaluated. For this purpose, the spectra in the NIR range (1100-2100 nm) of 240 intact berry samples were acquired in the laboratory. From these same samples, the concentration of volatile compounds was analyzed by thin film-solid-phase microextraction-gas chromatography-mass spectrometry (TF-SPME-GC-MS), and the TSS were quantified by refractometry. These two methods were used as reference methods for model building. Calibration, cross-validation and prediction models were built from spectral data using partial least squares (PLS). Determination coefficients of cross-validation (R2 CV ) above 0.5 were obtained for all volatile compounds, their families, and TSS.

CONCLUSIONS

These findings support that NIR spectroscopy can be successfully use to estimate the aromatic composition as well as the TSS of intact Tempranillo Blanco berries in a non-destructive, fast, and contactless form, allowing simultaneous determination of technological and aromatic maturities. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Tannat grape pomace as an ingredient for potential functional biscuits: bioactive compound identification, in vitro bioactivity, food safety, and sensory evaluation

Grape pomace, the main by-product of wine process, shows high potential for the development of functional foods, being a natural source of bioactive compounds and dietary fiber. Thus, the present study proposes the development of five potential functional biscuits. The five formulations were achieved by varying the Tannat grape pomace powder (TGP, 10-20% w/w total wet dough) and sweetener sucralose (2-4% w/w total wet dough) content through a factorial design with central points. TGP microbiological and pesticides analysis were performed as a food safety requirement. Identification of bioactive compounds by HPLC-DAD-MS, in vitro bioactivity (total phenol content, antioxidant by ABTS and ORAC-FL, antidiabetic and antiobesity by inhibition of α-glucosidase and pancreatic lipase, respectively) and sensory properties of the biscuits were evaluated. TGP microbiological and pesticides showed values within food safety criteria. Sensory profiles of TGP biscuits were obtained, showing biscuits with 20% TGP good sensory quality (7.3, scale 1-9) in a cluster of 37 out of 101 consumers. TGP addition in biscuits had a significant (p < 0.05) effect on total phenolic content (0.893-1.858 mg GAE/g biscuit) and bioactive properties when compared to controls: 11.467-50.491 and 4.342-50.912 μmol TE/g biscuit for ABTS and ORAC-FL, respectively; inhibition of α-glucosidase and pancreatic lipase, IC50 35.572-64.268 and 7.197-47.135 mg/mL, respectively. HPLC-DAD-MS results showed all the identified phenolic compounds in 20/4% biscuit (TGP/sucralose%) were degraded during baking. Malvidin-3-O-(6'-p-coumaroyl) glucoside, (+)-catechin, malvidin-3-O-glucoside, and (-)-epicatechin were the main phenolic compounds (in descendent order of content) found. The bioactive properties could be attributed to the remaining phenolic compounds in the biscuits. In conclusion, TGP biscuits seemed to be a promising functional food with potential for ameliorating oxidative stress, glucose and fatty acids levels with good sensory quality.

Prediction of sensory attributes in winemaking grapes by on-line near-infrared spectroscopy based on selected volatile aroma compounds

Aroma represents an important quality aspect for wine. The aroma of different grapes and wines is formed by the varying composition and concentrations of numerous aroma compounds, which result in different sensory impressions. The analysis of aroma compounds is usually complex and time-consuming, which requires the development of rapid alternative methods. In this study, grape mash samples were examined for aroma compounds, which were released under tasting conditions. A selection of the determined aroma compounds was grouped according to their sensory characteristics and calibration models were developed for the determination of sensory attributes by near-infrared (NIR) spectroscopy. The calibration models for the selected sensory attributes "fruity," "green," "floral" and "microbiological" showed very high prediction accuracies (0.979 < R²_C < 0.996). Moreover, four different grape model solutions, whose compositions were based on the results from GC-MS-based analysis of the grape mash samples, were examined in a sensory evaluation. Despite large variation of the single values, the averaged values of the given scores for intensity of odour and taste showed differences between the model solutions for most of the evaluated sensory attributes. Sensory analysis remains essential for the evaluation of the overall aroma; however, NIR spectroscopy can be used as an additional and more objective method for the estimation of possible desired or undesired flavour nuances of grape mash and the quality of the resulting wine.

Rapid Determination of Geniposide and Baicalin in Lanqin Oral Solution by Near-Infrared Spectroscopy with Chemometric Algorithms during Alcohol Precipitation

The selection of key variables is an important step that improves the prediction performance of a near-infrared (NIR) real-time monitoring system. Combined with chemometrics, NIR spectroscopy was employed to construct high predictive accuracy, interpretable models for the rapid detection of the alcohol precipitation process of Lanqin oral solution (LOS). The variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) was innovatively introduced into the variable screening process of the model of geniposide and baicalin. Compared with the commonly used synergy interval partial least squares regression, competitive adaptive reweighted sampling, and random frog, VCPA-IRIV achieved the maximum compression of variable space. VCPA-IRIV-partial least squares regression (PLSR) only needs to use about 1% of the number of variables of the original data set to construct models with Rp values greater than 0.95 and RMSEP values less than 10%. With the advantages of simplicity and strong interpretability, the prediction ability of the PLSR models had been significantly improved simultaneously. The VCPA-IRIV-PLSR models met the requirements of rapid quality detection. The real-time detection system can help researchers to understand the quality rules of geniposide and baicalin in the alcohol precipitation process of LOS and provide a reference for the optimization of a LOS quality control system.