Robust NIRS models for non-destructive prediction of mango internal quality

Nordey, Thibault; Joas, Jacques; Davrieux, Fabrice; Chillet, Marc; Léchaudel, Mathieu

doi:10.1016/j.scienta.2016.12.023

Cited by 82 publications

(22 citation statements)

References 37 publications

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“…In addition to the above-mentioned methods, visible and near-infrared (vis/NIIR) is also widely used by researchers, as one of the most successful non-destructive methods for measuring chemical components and quality characteristics of fruits and vegetables [12][13][14][15]. The visible and near-infrared (vis/NIIR) spectroscopy is applied on different fruits such as apricot [16], olive [17], pear [18], apple [19], grapefruit [20], jujube [21], and tomato [22]. Measuring the strength of the pulp of fruits is one of the applications of the visible and near-infrared (vis/NIIR) spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

et al. 2019

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Nondestructive estimation of the various physicochemical features of food such as fruits and vegetables will create a dramatic development in the food industry. The reason for this development is that the estimation is non-destructive, online, and most importantly fast. Regarding the advantages, various researchers have focused on how to undertake non-destructive estimation of the physicochemical features of various nutrients. Three main goals were pursued in this article. These are: 1. Nondestructive estimation of the chlorophyll b content of red delicious apple using color features and hybrid artificial neural network-cultural algorithm (ANN-CA), 2. Nondestructive estimation of chlorophyll b content of red delicious apple using spectral data (around a range of 680 nm) and hybrid Artificial Neural Network-biogeography-based algorithm (ANN-BBO), 3. Nondestructive estimation of the chlorophyll b content of red delicious apple using different groups of selective spectra by the hybrid artificial neural network-differential evolution algorithm (ANN-DA). In each of these methods, 1000 replications were performed to evaluate the reliability of various hybrids of the artificial neural network. Finally, the results indicated that the average determination coefficient in 1000 replications for the hybrid artificial neural network, the cultural algorithm, and the hybrid artificial neural network, the biogeography-based optimization algorithm, was 0.882 and 0.932, respectively. Also, the results showed that the highest value of the coefficient of determination among the different groups of effective features is related to the group of features with 10 spectra. The coefficient of determination in this case was 0.93.

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Section: Introductionmentioning

confidence: 99%

Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

et al. 2019

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“…During the quality detection of jujube fruits by spectrum, the external qualities (such as bruises, insect infestations, and crack features) and internal qualities (such as SSC and hardness) were researched [10–14], and the resulting models were primarily established by individual maturity stage. However, the fruit ripening process was influenced by differences in external factors, such as light, nutrients, temperature, flowering time, and environmental conditions in different fruit-bearing branches [15–17]. The harvested fruit is the product of all the maturity stages.…”

Section: Introductionmentioning

confidence: 99%

Detection of the Soluble Solid Contents from Fresh Jujubes during Different Maturation Periods Using NIR Hyperspectral Imaging and an Artificial Bee Colony

Sun

Zhang

Chen

et al. 2019

Journal of Analytical Methods in Chemistry

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To perform accurate and synchronous detection of the soluble solid contents (SSC) in fresh jujubes at different stages of maturity, hyperspectral imaging was used to establish robust models. The combined data constituting four maturation stages were used to build the grid-search least squares support vector machine (GS-LS-SVM) model. The determination coefficient (Rp2), the root-mean-square error (RMSEP), and the residual predictive deviation (RPD) of the prediction set for samples of the overall stages were 0.98, 1.10%, and 7.85, respectively. Furthermore, a successive projections algorithm (SPA) was used to extract the characteristic wavelengths of the combined data. An artificial bee colony (ABC) algorithm (for the prediction set, Rp2 = 0.98, RMSEP = 1.19%, RPD = 7.25) was used to improve the SPA-LS-SVM model, which was better than the SPA-GS-LS-SVM model (for the prediction set, Rp2 = 0.98, RMSEP = 1.24%, RPD = 6.96). Lastly, visualization of the SSC distribution map was performed based on the SPA-ABC-LS-SVM model, which clearly showed that the SSC gradually increased during maturation. The results indicated that it was realistic to construct a detection model of the multimaturity stage. This research also demonstrated that the combination of hyperspectral imaging and the ABC had good application values in the testing of agricultural products.

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“…With increasing demands from consumers and the market, it is necessary for growers to monitor muskmelon internal phenotypes without fruit destruction. Previous studies [1, 2] have shown that the muskmelon external phenotypes (i.e., fruit color and skin netting) and internal phenotypes are closely related. Thus, the non-destructive testing of fruit internal phenotypes can be achieved through monitoring fruit appearance, namely, through fruit external phenotypes.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive monitoring of netted muskmelon quality based on its external phenotype using Random Forest

et al. 2019

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The internal phenotypes of netted muskmelon ( Cucumis melo L. var . eticulates Naud.) are always associated with its external phenotypes. In this study, the parameters of external phenotypic traits were extracted from muskmelon images captured by machine vision, and the internal phenotypes of interest to us were measured. Pearson analysis showed that most external phenotypic traits were highly correlated with these internal phenotypes in muskmelon fruit. In this study, we used the random forest algorithm to predict muskmelon fruit internal phenotypes based on the significantly associated external parameters. Carotenoids, sucrose, and total soluble solid (TSS) were the three most accurately monitored internal phenotypes with prediction R -squared ( R 2 ) values of 0.947 (root-mean-square error (RMSE) = 0.019 mg/100 g), 0.918 (RMSE = 3.233 mg/g), and 0.916 (RMSE = 1.089%), respectively. Further, a simplified model was constructed and validated based on the top 10 external phenotypic parameters associated with each internal phenotype, and these parameters were filtered with the varImp function from the random forest package. The top 10 external phenotypic parameters correlated with each internal phenotype used in the simplified model were not identical. The results showed that the simplified models also accurately monitored the melon internal phenotypes, despite that the predicted R 2 values decreased 0.3% to 7.9% compared with the original models. This study improved the efficiency and accuracy of real-time fruit quality monitoring for greenhouse muskmelon.

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Robust NIRS models for non-destructive prediction of mango internal quality

Cited by 82 publications

References 37 publications

Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

Detection of the Soluble Solid Contents from Fresh Jujubes during Different Maturation Periods Using NIR Hyperspectral Imaging and an Artificial Bee Colony

Non-destructive monitoring of netted muskmelon quality based on its external phenotype using Random Forest

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