Classification of intact açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Cunha, Luís Carlos; Nardini, Viviani; Khatiwada, Bed P.; Teixeira, Gustavo Henrique de Almeida; Walsh, Kerry B.

doi:10.1016/j.foodcont.2014.09.046

Cited by 21 publications

(7 citation statements)

References 17 publications

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“…In the raw spectra without preprocessing, there is usually a variation of the baseline caused mainly by the presence of additive and multiplicative scattering of light. Thus, the preprocessing of the NIR spectra was used to adjust the variation related to light scattering, baseline shift, and multiplicative dispersion (Cunha Junior et al 2015; Snel et al 2018). As Figure 3A shows, the difference between the NIR spectra was mainly due to a marked deviation from the baseline between species; this difference was corrected by applying SNV preprocessing (Figure 3B).…”

Section: Resultsmentioning

confidence: 99%

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Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

Braga

Chiconi²,

Bacha³

et al. 2023

Weed Sci

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The occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which, in the future, might be coupled to machines to optimize the performance of a species-specific weed management. Thus, the objective of this study was to use near infrared spectroscopy (NIR) and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of three weed species in vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm-1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden’s index and analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of pre-processing was performed. It was possible to obtain an accuracy of 99.3, 98.5, and 98.7% for ivylead morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth] and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA), or associated with partial least squares regression with discriminant analysis (PLS-DA), can be used to discriminate morningglory species.

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Section: Resultsmentioning

confidence: 99%

“…The study of NIR spectral bands (Figure 3) enabled identification of the main chemical groups of certain spectral bands (Cunha Junior et al 2015; Pasquini 2003). The peaks observed in the region of 5,000 and 7,000 cm −1 corresponded to O-H vibrations; in this case, water is primarily the cause for these peaks.…”

Section: Resultsmentioning

confidence: 99%

Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

Braga

Chiconi²,

Bacha³

et al. 2023

Weed Sci

Self Cite

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“…This technique uses the partial least square regression methods to perform discriminant analysis, and this technique is especially useful for large data sets, high‐dimensional data sets (Naes and others ). The accuracy of the discriminant models were evaluated according to Cunha Junior and others ().…”

Section: Methodsmentioning

confidence: 99%

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

Canneddu

Júnior

Teixeira

2016

Journal of Food Science

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The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near-infrared (FT-NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA-LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (Rp (2) ) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, Rp (2) = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT-NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.

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“…The resulting performance was evaluated according to the percentage of correct classication, as reported by Cunha Júnior et al: 24 Correct classification ð%Þ ¼ 100…”

Section: Discriminant Analysis and Variable Selection Techniquesmentioning

confidence: 99%

“…Chemometric approaches e.g., principal component analysis (PCA), 17 partial least squares (PLS), 18 linear discriminant analysis (LDA), 19 genetic algorithm, 20 and successive projections algorithm (SPA) 21 permit the processing of large amounts of spectroscopic data variables that subsequently require data reduction approaches in order to identify sources of variance across spectra and inter-class variation. Indeed, the NIR technique has been used to discriminate alcoholic beverages, e.g., wine produced in different geographic regions 22,23 and to predict and quantify chemical components in food, 24 the wine industry, [25][26][27] and in the beer industry. 28 Considering the possibilities of NIR spectroscopy, the objective of this study was to develop a non-destructive method using NIR spectroscopy and chemometric tools to validate the authenticity of sugarcane aguardentes produced in two geographic regions (São Paulo and Minas Gerais states), as well as to predict the ethanol content in both spirits, aiming for this method to be used as an analytical tool to evaluate fraud and validate the authenticity of these spirits.…”

Section: Introductionmentioning

confidence: 99%

Determination of the geographical origin and ethanol content of Brazilian sugarcane spirit using near-infrared spectroscopy coupled with discriminant analysis

et al. 2016

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18Aguardente is a typical Brazilian spirit produced by the distillation of sugarcane most. The 19 valorisation of this spirit can be attributed to its notoriety, related to the production origin which can 20 influence its quality. Therefore, the objective of this study was to use NIR spectroscopy coupled with 21 discriminant analysis as a non-destructive method to attribute authenticity of aguardentes produced 22 in two geographic regions and to predict the ethanol content. Some chemometric methods were used 23 to discriminate sugarcane aguardente, namely partial least squares-linear discriminant analysis 24 (PLS-LDA), principal component analysis-linear discriminant analysis (PCA−LDA), and variable 2 selection techniques such as successive projection algorithm (SPA−LDA) and genetic algorithm 26 (GA−LDA). NIR spectra were collected using a FT−NIR spectrometer (4,000-10,000 cm -1 ) at a 27 spectral resolution of 16 cm -1 , 8 cm -1 interval, and 64 scans. PCA results were not effective to classify 28 the aguardente samples, but with PLS−DA, PCA−LDA, SPA−LDA, GA−LDA and LDA it was 29 possible to get 87.2% prediction accuracy. Better results were obtained using PLS−DA on raw 30 spectra and GA-LDA using only six wavelengths (namely, 1,025 nm, 1,181 nm, 1,596 nm, 1,610 nm, 31 1,653 nm, 2,125 nm) which gave relatively good accuracy rate (up to 87.2 %). NIR spectroscopy and 32 chemometrics can be used as a non-destructive method to attribute authenticity, and PLSR combined 33 with NIR was a good non-destructive method to predict ethanol content in sugarcane aguardente. 34 35 while the Minas Gerais State is the main alembic producer, both representing 25% of the Brazilian 46 market 3 . In the alembic production of sugarcane spirit the product is divided into three fractions: 47 head, heart, and tail, while in column distillation there is no such separation into fractions and that 48 explains to a range extent the chemical differences produced by the two processes 4 . 49

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Classification of intact açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Cited by 21 publications

References 17 publications

Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

Determination of the geographical origin and ethanol content of Brazilian sugarcane spirit using near-infrared spectroscopy coupled with discriminant analysis

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