Application of Vis/NIR spectroscopy for predicting sweetness and flavour parameters of ‘Valencia’ orange (Citrus sinensis) and ‘Star Ruby’ grapefruit (Citrus x paradisi Macfad)

Ncama, Khayelihle; Opara, Umezuruike Linus; Tesfay, S.Z.; Fawole, Olaniyi Amos; Magwaza, Lembe Samukelo

doi:10.1016/j.jfoodeng.2016.08.015

Cited by 99 publications

(45 citation statements)

References 32 publications

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“…To date, Vis-NIR spectroscopy and related approach have been successfully applied to develop theoretical models for the discrimination of shelf-life or storage stages of freshcut salads and apples (Beghi, Giovenzana, Civelli, & Guidetti, 2016), peaches (Huang, Meng, Zhu, & Wu, 2017), pears (He, Fu, Rao, & Fang, 2016), and Valerianella locusta L. (Giovenzana, Beghi, Buratti, Civelli, & Guidetti, 2014). Post-harvest quality, such as SSC (soluble solids content), dry matter, pH, and firmness, was also investigated in tomatoes (Huang, Lu, & Chen, 2018), oranges (Ncama, Opara, Tesfay, Fawole, & Magwaza, 2017), sweet cherries (Escribano, Biasi, Lerud, Slaughter, & Mitcham, 2017), plums (Li, Pullanagari, Pranamornkith, Yule, & East, 2017), pears (Wang, Wang, Chen, & Han, 2017), and kiwifruits (Li et al, 2017) by Vis/NIR spectroscopy. In strawberries, a limited literature on the use of Vis/NIR related techniques for quality estimation has been found.…”

Section: Introductionmentioning

confidence: 99%

On‐line discrimination of storage shelf‐life and prediction of post‐harvest quality for strawberry fruit by visible and near infrared spectroscopy

Shen

Zhang

Cao

et al. 2018

J Food Process Engineering

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Quick assessment of storage time is important for the guarantee of fruit shelf‐life. On‐line discrimination of refrigerated storage time and prediction of post‐harvest quality for strawberry fruit was carried out by visible and near‐infrared (Vis/NIR) spectroscopy. For the classification of samples with storage time of 1, 3, 5, and 7 days, partial least square discriminant analysis (PLS‐DA) achieved correct classified rates of 93.3–97.4% in prediction based on selected wavelengths obtained by competitive adaptive reweighted sampling (CARS) at speeds of 0.05, 0.10, and 0.15 m/s. Moreover, for prediction of soluble solids content (SSC) in strawberry, the determination coefficient (rp2), root mean square error of prediction (RMSEP), and RPD value obtained by PLS coped with CARS at fruit moving speed of 0.10 m/s was 0.733, 0.699 °Brix and 1.96, respectively. Overall, this study indicates that Vis–NIR spectroscopy offers the potential as an on‐line tool for quality assurance of strawberry fruit. Practical applications Visible and near‐infrared (Vis/NIR) spectroscopy is a very attractive technique for nondestructive evaluation of quality of food products. Strawberry has an extremely short shelf‐life which is estimated to be less than 5 days. Rapid identification its postharvest storage time is essential for the evaluation of its shelf‐life. Based on the results reported here, Vis–NIR has the potential for on‐line monitoring of postharvest storage time and post‐harvest quality for strawberry fruit in real situation.

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Section: Introductionmentioning

confidence: 99%

On‐line discrimination of storage shelf‐life and prediction of post‐harvest quality for strawberry fruit by visible and near infrared spectroscopy

Shen

Zhang

Cao

et al. 2018

J Food Process Engineering

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“…Due to the change in the value of chlorophyll b during the ripening stages of fruits, especially red delicious apple, its non-destructive estimation will be useful for predicting the ripening stage [16,35]. To measure the actual amount of chlorophyll b, the method used by Ncama et al [20] was used. Based on this method, the formula for calculating the value of chlorophyll b is based on Equation 3.…”

Section: Extraction Of Chlorophyll Bmentioning

confidence: 99%

“…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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“…Due to some noises caused by environment, equipment, samples and baseline drift have a negative impact on the performance of the model, another advantage of SDAE-NN is its denoising ability compared with PLSR.. The PLSR method used in the previous reports [4,33] needs to perform spectral preprocessing such as multiplicative scatter correction and standard normal variate before modeling to eliminate noise. In this paper, we added some Gaussian noise in the autoencoder process to improve the robustness of the neural network ( Figure 10) and to enhance the antinoise ability of the model.…”

Section: +1mentioning

confidence: 99%

Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon

Zhong

Yang

2018

Journal of Spectroscopy

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The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R 2 test ) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R 2 test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.

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Application of Vis/NIR spectroscopy for predicting sweetness and flavour parameters of ‘Valencia’ orange (Citrus sinensis) and ‘Star Ruby’ grapefruit (Citrus x paradisi Macfad)

Cited by 99 publications

References 32 publications

On‐line discrimination of storage shelf‐life and prediction of post‐harvest quality for strawberry fruit by visible and near infrared spectroscopy

On‐line discrimination of storage shelf‐life and prediction of post‐harvest quality for strawberry fruit by visible and near infrared spectroscopy

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

Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon

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