Evaluation of Near-Infrared and Mid-Infrared Spectroscopy for the Determination of Routine Parameters in Chinese Rice Wine

Shen, Fei; Wu, Qingbiao; Wei, Yingqi; Liu, Xiao; Tang, Pei-An

doi:10.1111/jfpp.12952

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…The correlation coefficient (Rv 2 ) and root mean square error or prediction (RMSEP) were used for prediction criteria. The ability of PLSR calibration models to estimate or predict the physicochemical and sensorial parameters from FTIR spectra was confirmed by external validation (Shen, Wu, Wei, Liu, & Tang, 2017).…”

Section: Chemometric Methodsmentioning

confidence: 86%

“…PLSR model was performed to obtain a representative calibration of the FTIR spectral data with respect to physicochemical and sensory parameter of the sauces samples. This calibration works with the information obtained from the whole spectra to develop the regression equation between FTIR spectra and values of the analytes of interest (Shen et al, ). PLSR has shown to be the best model to predict physicochemical parameters in Chinese rice wine (Shen et al, ) and cooking oil (Wu, 2015) compared to other models such as multiple linear regression or principal component regression, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…This calibration works with the information obtained from the whole spectra to develop the regression equation between FTIR spectra and values of the analytes of interest (Shen et al, ). PLSR has shown to be the best model to predict physicochemical parameters in Chinese rice wine (Shen et al, ) and cooking oil (Wu, 2015) compared to other models such as multiple linear regression or principal component regression, respectively. The summary of the statistics obtained of the calibration, cross‐validation, and external validation on FTIR spectra with respect physicochemical and sensorial parameters is presented in Table .…”

Section: Resultsmentioning

confidence: 99%

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Food quality evaluation of accelerated shelf life of chili sauce using Fourier transform infrared spectroscopy and chemometrics

Rodiles‐López

García‐Rodríguez

Gómez‐Orozco

et al. 2019

J Food Process Preserv

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Recently, spectroscopy analysis has been used as a rapid chemical analysis method of food products. To evaluate the potentiality of Infrared spectroscopy (IR) in food analysis during storage, an accelerated shelf‐life study was carried out on three types of chili habanero sauces at 50°C for a period of 24 days. Only some physicochemical and sensory evaluation showed changes during chili sauces storage. FT‐IR spectra tandem with principal component analysis (PCA) allow differentiating between a fresh sauce and a long‐time prepared one. Partial least squares model showed that FT‐IR data can be compared to some physicochemical and sensorial parameters. Results of sensorial and spectrophotometry analyses can be used to calculate the shelf life of the sauces. This is the first study that reported about the use of a rapid and low‐cost method of FT‐IR tandem chemometrics as the food quality evaluation of accelerated shelf life of a chili sauces‐based vegetable ingredients. Practical applications Food processing requires faster, more efficient, and easier to use tools to evaluate the quality of its ingredients, the monitoring of the process, and the quality of the final product, as well as methods to monitor the freshness of the products during storage. This study suggests that the infrared spectroscopy together with the chemometric analysis could be a viable and fast alternative, which is given in a single step to cover the needs of quality monitoring of products both in the development of new products and in the process in the food industry.

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Section: Chemometric Methodsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Food quality evaluation of accelerated shelf life of chili sauce using Fourier transform infrared spectroscopy and chemometrics

Rodiles‐López

García‐Rodríguez

Gómez‐Orozco

et al. 2019

J Food Process Preserv

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“…Nutrients in freeze‐dried A. bisporus mainly consisted of carbohydrate, protein, and small amount of fat. The NIR spectra showed a high absorption peak in the region of 7313–6035 cm −1 , mainly related to the stretch and deformation of the O─H in water or carbohydrate . This was due to a water increase from the storage environment, and fungal infection that could reduce the carbohydrate content of food products.…”

Section: Resultsmentioning

confidence: 99%

Detection and identification of fungal growth on freeze‐dried Agaricus bisporus using spectra and olfactory sensors

Wang

Pei

et al. 2020

J Sci Food Agric

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BACKGROUND Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near‐infrared (NIR) spectroscopy, mid‐infrared (MIR) spectroscopy, and an electronic nose (E‐nose) were applied to detect and identify freeze‐dried Agaricus bisporus infected with the five fungi. RESULTS Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (Rc2) = 0.786, root mean‐square error of calibration (RMSEC) = 0.125 log CFU g−1. The A. flavus group had the highest prediction performance: coefficient of prediction (Rp2) = 0.821, root mean‐square error of prediction (RMSEP) = 0.083 log CFU g−1. The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E‐nose results for freeze‐dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS‐DA) using NIR, MIR, and E‐nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E‐nose methods. CONCLUSION Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E‐nose were proven to be effective in monitoring fungal contamination on freeze‐dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry

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“…Martelo-Vidal and Vázquez [47] applied NIR to predict ethanol in wines (R 2 0.991 and RMSEP 1.78 g/L). Shen et al [56] determined alcohol degree in rice wine using NIR-PLSR (R 2 0.972, RMSECV 0.393), MIR-PLSR (R 2 0.956, RMSECV 0.494). The prediction accuracy of the present MIR-PLS (R 2 0.999 and RMSEP 0.1%, w/w) is comparable to or even better than similar studies in wine, beer and spirit drinks.…”

Section: Resultsmentioning

confidence: 99%

Non-destructive determination of ethanol levels in fermented alcoholic beverages using Fourier transform mid-infrared spectroscopy

Debebe

Redi‐Abshiro

Chandravanshi

2017

Chemistry Central Journal

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BackgroundTraditional fermented alcoholic beverages are indigenous to a particular area and are prepared by the local people using an age-old techniques and locally available raw materials. The main objective of this work was the direct determination of ethanol in traditional fermented alcoholic beverages using mid infrared spectroscopy with partial least squares regression, verifying the robustness of the calibration models and to assess the quality of beverages.ResultsThe level of ethanol determination in Ethiopian traditional fermented alcoholic beverages was done using mid infrared spectroscopy with partial least squares regression (MIR-PLS). The calibration and validation sets, and real samples spectra were collected with 32 scans from 850–1200 cm−1. A total of 25 synthetic standards (calibration and validation sets) with ethanol (2–10% w/w) and sugars (glucose, fructose, sucrose and maltose) (0–5% w/w) compositions were used to construct and validate the models. Twenty-five different calibration models were validated by cross-validation approach with 25 left out standards. A large number of pre-treatments were verified, but the best pre-treatment was subtracting minimum + 2nd derivative. The model was found to have the highest coefficients of determination for calibration and cross-validation (0.999, 0.999) and root mean square error of prediction [0.1% (w/w)]. For practical relevance, the MIR-PLS predicted values were compared against the values determined by gas chromatography. The predicted values of the model were found to be in excellent agreement with gas chromatographic measurements. In addition, recovery test was conducted with spiking 2.4–6.4% (w/w) ethanol. Based on the obtained recovery percentage, 85.4–107% (w/w), the matrix effects of the samples were not considerable.ConclusionThe proposed technique, MIR-PLS at 1200–850 cm−1 spectral region was found appropriate to quantify ethanol in fermented alcoholic beverages. Among the studied beverages (Tella, Netch Tella, Filter Tella, Korefe, Keribo, Borde and Birz), the average ethanol contents ranged from 0.77–9.1% (v/v). Tej was found to have the highest ethanol content whereas Keribo had the least ethanol content. The developed method was simple, fast, precise and accurate. Moreover, no sample preparation was required at all. However, it should be noted that the present procedure is probably not usable for regulatory purposes (e.g. controlling labelling).

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Evaluation of Near-Infrared and Mid-Infrared Spectroscopy for the Determination of Routine Parameters in Chinese Rice Wine

Cited by 9 publications

References 38 publications

Food quality evaluation of accelerated shelf life of chili sauce using Fourier transform infrared spectroscopy and chemometrics

Food quality evaluation of accelerated shelf life of chili sauce using Fourier transform infrared spectroscopy and chemometrics

Detection and identification of fungal growth on freeze‐dried Agaricus bisporus using spectra and olfactory sensors

Non-destructive determination of ethanol levels in fermented alcoholic beverages using Fourier transform mid-infrared spectroscopy

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