Classification of jaboticaba fruits at three maturity stages using NIRS and LDA

Costa, Rosangela Câmara; Cunha, Luís Carlos; Morgenstern, Thayara Bittencourt; Teixeira, Gustavo Henrique de Almeida; Lima, Kássio M. G.

doi:10.1039/c5ay03212a

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Jabuticaba is very well known in South America, especially in Brazil (Nascimento et al, 2013). This fruit is produced twice a year and the average yield of a mature tree can be over 1000 pounds of fruit (Costa et al, 2016;Teixeira et al, 2011). This fruit is dark and has a spherical shape with a small size (3.0-4.0 cm) and it is highly appreciated due to its relevant nutritional properties (Silva et al, 2008;Wu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Optimization of ultrasound-assisted extraction of bioactive compounds from jabuticaba (Myrciaria cauliflora) fruit through a Box-Behnken experimental design

et al. 2019

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Jabuticaba is a very popular fruit in Brazil being a great source of compounds with considerable biological activities. Novel optimized ultrasound-assisted extraction (UAE) methods have been proposed for anthocyanins and total phenolic compounds from jabuticaba. A Box-Behnken experimental design (BBD) with a response surface methodology (RSM) was used to investigate the effect of six independent variables (solvent composition, solvent-to-sample ratio, ultrasound amplitude and cycle, pH, and temperature) on the UAE. Solvent composition was found to be the most significant variable for the extraction of both anthocyanins (51%) and total phenolic compounds (72%). The other optimum conditions for anthocyanins were as follows: pH 7.00, 39.8 ºC, 20:1.5 mL:g solvent-to-sample ratio, 34% ultrasound amplitude and cycle of 0.47 seconds. The optimum conditions for the extraction of phenolic compounds were: pH 7.00, 26.0 ºC, 20:1.5 (mL:g) solvent-to-sample ratio, 68.5% ultrasound amplitude and cycle of 0.5 seconds. The extraction kinetic was also evaluated. The developed methods showed a high precision, with coefficients of variation of less than 5% for both repeatability and intermediate precision (within-lab reproducibility). The applicability of the new methods was successfully evaluated on several fruits and jams from jabuticaba.

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Section: Introductionmentioning

confidence: 99%

Optimization of ultrasound-assisted extraction of bioactive compounds from jabuticaba (Myrciaria cauliflora) fruit through a Box-Behnken experimental design

et al. 2019

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“…For the development of the classification models, principal component analysis (PCA) with linear discriminate analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), successive projection algorithm (SPA) with LDA (SPA-LDA), and a genetic algorithm (GA) with LDA (GA-LDA) (Costa et al, 2016) were used. The optimum number of variables for SPA-LDA and GA-LDA was obtained using the average G risk of incorrect classification of LDA.…”

Section: Classification Model Developmentmentioning

confidence: 99%

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Leite

Corrêa

Júnior

et al. 2020

Journal of Food Composition and Analysis

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In soybean (Glycine max L.) breeding programs, segregation is normally observed, and it is not possible to have replicates of individuals because each genotype is a unique copy. Therefore, near-infrared spectroscopy (NIRS) was used as a nondestructive tool to classify soybeans by genotypes and to predict oil content. A total of 260 soybean genotypes were divided into five classes, which were composed of 32, 52, 82, 46, and 49 samples of the BV, BVV, EB, JAB, and L class, respectively. NIR spectra were obtained using oven-dried samples (80 g) in a reflectance mode. A successive projection algorithm and genetic algorithm with linear discriminant analysis discriminated genotypes of the low (L class) from the high (EB class) for oil content (88.89% accuracy). The partial least square regression models for oil content were considered good (root mean square error of prediction of 0.96%). Therefore, NIRS can be used as a non-destructive tool in soybean breeding programs, but further investigation is necessary to improve the robustness of the models. It is important to note that to use the models, it is necessary to collect NIR spectra from dry soybean samples.

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Green Processes in Foodomics: Biorefineries in the Food Industry

Saldaña

Silva

Cornejo

et al. 2021

Comprehensive Foodomics

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Classification of jaboticaba fruits at three maturity stages using NIRS and LDA

Abstract: This study proposes a rapid and non-destructive method of jaboticaba [Myrciaria cauliflora (Mart.) O. Berg] fruit classification at three maturity stages using Near-Infrared Reflectance Spectroscopy (NIRS) combined with principal component analysis-linear discriminant analysis (PCA-LDA).

Cited by 5 publications

References 16 publications

Optimization of ultrasound-assisted extraction of bioactive compounds from jabuticaba (Myrciaria cauliflora) fruit through a Box-Behnken experimental design

Optimization of ultrasound-assisted extraction of bioactive compounds from jabuticaba (Myrciaria cauliflora) fruit through a Box-Behnken experimental design

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Green Processes in Foodomics: Biorefineries in the Food Industry

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