Assessing Fermentation Broth Quality of Pineapple Vinegar Production with a Near-Infrared Fiber-Optic Probe Coupled with Stability Competitive Adaptive Reweighted Sampling.

In this study, the performance of a near-infrared (NIR) fiber-optic probe coupled with stability competitive adaptive reweighted sampling (SCARS) was investigated for the analysis of acetic acid, ethanol, total soluble solids, caffeic acid, gallic acid, and tannic acid in the broth of pineapple vinegar during fermentation. The NIR spectra of the broth samples in the region of 11,536-3956 cm-1 were collected during vinegar fermentation promoted by Acetobacter aceti. This continuous biological process led to changes in the concentrations of all analytes studied. SCARS provided optimized and stabilized NIR spectral variables for the construction of a partial least squares (PLS) model for each analyte using a small number of optimal variables (under 88 variables). The SCARS-PLS model outperformed the conventional PLS model, and achieved excellent accuracy in accordance with ISO 12099:2017 for the four prediction models of acetic acid, ethanol, caffeic acid, and gallic acid, with root-mean-square error of prediction values of 0.137%, 0.178%, 0.637 µg/mL and 0.640 µg/mL, respectively. In contrast, only an acetic acid content prediction model constructed via the conventional PLS method and using the whole spectral region (949 variables) could pass with acceptable accuracy. These results indicate that the NIR optical probe coupled with SCARS is an appropriate method for the continuous monitoring of multianalytes during vinegar fermentation, particularly acetic acid and ethanol contents, which are indicators of the finished fermentation of pineapple vinegar.

Simultaneous Monitoring of the Evolution of Chemical Parameters in the Fermentation Process of Pineapple Fruit Wine Using the Liquid Probe for Near-Infrared Coupled with Chemometrics.

This study used Fourier transform-near-infrared (FT-NIR) spectroscopy equipped with the liquid probe in combination with an efficient wavelength selection method named searching combination moving window partial least squares (SCMWPLS) for the determination of ethanol, total soluble solids, total acidity, and total volatile acid contents in pineapple fruit wine fermentation using Saccharomyces cerevisiae var. burgundy. Two fermentation batches were produced, and the NIR spectral data of the calibration samples in the wavenumber range of 11,536-3952 cm-1 were obtained over ten days of the fermentation period. SCMWPLS coupled with second derivatives searched and optimized spectral intervals containing useful information for building calibration models of four parameters. All models were validated by test samples obtained from an independent fermentation batch. The SCMWPLS models showed better predictions (the lowest value of prediction error and the highest value of residual predictive deviation) with acceptable statistical results (under confidence limits) among the results achieved by using the whole region. The results of this study demonstrated that FT-NIR spectroscopy using a liquid probe coupled with SCMWPLS could select the optimized wavelength regions while reducing spectral points and increasing accuracy for simultaneously monitoring the evolution of four chemical parameters in pineapple fruit wine fermentation.

Rapid selection of Andrographis paniculata medicinal plant materials based on major bioactive using near-infrared spectroscopy.

Abstract: The quantitative analysis of andrographolides in Andrographis paniculata plant materials is essential for pharmaceutical factories. This analysis cannot be done for all samples due to the conventional process using the extraction and HPLC methods requires a long analysis time and sample destruction. Therefore, near-infrared spectroscopy (NIRS) was employed to classify the class of A. paniculata and to determine the content of two active ingredients, andrographolide (AP1) and dehydroandrographolide (AP3) in A. paniculata, rapidly and non-destructively. One hundred twenty dried powder samples were obtained from aerial parts, branches, leaves, and branches mixed with leaves. The NIR absorption scans were collected from a broad spectral region (1000-2500 nm). Then, the scanned samples were extracted and analyzed for their AP1 and AP3 contents using an HPLC reference method. The success classification model based on AP1 level was developed using the second derivative pretreated NIR spectra of the entire wavelength region using the Partial Least Squares-Discriminant Analysis (PLS-DA) method. The NIR calibration models were developed and tested for quantitative analysis with 50 independent samples. The models were identified for the analysis of the AP1 content with excellent performance (correlation coefficient (R) = 0.98; standard error of validation (SEV) = 0.24%) and for the analysis of the AP3 content at a good level of efficiency (R = 0.93; SEV = 0.15%). This study showed that NIR spectroscopic method offers rapid analysis for the selection of A. paniculata that meets the requirement in bioactive amount. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-021-01746-0.