Green analytical methodology for grape juice classification using FTIR spectroscopy combined with chemometrics

Junges, Carlos Henrique; Guerra, C. C.; Gomes, A. A.; Ferrão, Marco Flôres

doi:10.1016/j.talo.2022.100168

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…Partial least squares–linear discrimination analysis (PLS-LDA) is a multivariate analysis technique mainly used to deal with classification and discrimination problems; it combines the ideas of partial least squares regression (PLS) and linear discriminant analysis (LDA) [ 38 ]. PLS-LDA is a supervised discriminant analysis method where the dataset contains both an independent variable (X) and a dependent variable (Y), with the ability to model high-dimensional and high-correlation samples that capture the maximum variance of X associated with Y in the space.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

Guo,

Chen,

Zhang

et al. 2024

Foods

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Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.

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Section: Methodsmentioning

confidence: 99%

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

Guo,

Chen,

Zhang

et al. 2024

Foods

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“…The key difference between pomegranate and grape juice is noted in the region 1780-1685 cm −1 , which is attributed to C=O stretching. The very recent studies of Junges et al [75] are notable, reporting a green analytical methodology that involved solvent-free FTIR analysis of conventional and organic grape juices. The two types of grapes contain different sets of polyphenols and organic acids, which are identified in the FTIR measurements, and a precise classification was achieved by the multivariate statistical methods LDA and PLS-DA.…”

Section: Spectroscopy-based Approachesmentioning

confidence: 99%

Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages

Kumar

Castro

Feller

2023

Sensors

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Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.

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Quantitative analysis of adulterated pear juice based on attenuated total reflectance mid-infrared spectroscopy and bagging PLS

Tan,

Chen

2024

Journal of Food Composition and Analysis

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Green analytical methodology for grape juice classification using FTIR spectroscopy combined with chemometrics

Cited by 8 publications

References 38 publications

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages

Quantitative analysis of adulterated pear juice based on attenuated total reflectance mid-infrared spectroscopy and bagging PLS

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