Quality assessment and discrimination of intact white and red grapes from Vitis vinifera L. at five ripening stages by visible and near-infrared spectroscopy

Xiao, Hui; Li, Ang; Li, Meiyu; Sun, Yi; Tu, Kang; Wang, Shaojin; Pan, Leiqing

doi:10.1016/j.scienta.2018.01.041

Cited by 49 publications

(40 citation statements)

References 25 publications

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“…For this aim, several methods have been developed, such as uninformative variable elimination (UVE), genetic algorithms (GA), interval PLS (iPLS), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) (Zou, Zhao, Malcolm, Mel, & Mao, ). Xiao et al () applied CARS algorithm to select 40 key wavelengths from NIR spectroscopy for SSC prediction with r p 2 of 0.914 in grapes. Li et al () utilized GA and SPA algorithm to reduce the modeling complexity in SSC and pH prediction in cherry fruit by NIR hyperspectral imaging.…”

Section: Introductionmentioning

confidence: 99%

“…It is supported by both theoretical research and practical experience that appropriate wavelength selection is necessary for multivariate spectroscopic calibration (Fan et al, 2011). For this aim, several methods have been developed, such as uninformative variable elimination (UVE), genetic algorithms (GA), interval PLS (iPLS), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) (Zou, Zhao, Malcolm, Mel, & Mao, 2010). Xiao et al (2018 applied CARS algorithm to select 40 key wavelengths from NIR spectroscopy for SSC prediction with r p 2 of 0.914 in grapes.…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…Good SSC prediction models using NIRS were also reported for grapes, apple and other fruit . Therefore, NIRS is a feasible non‐destructive analytical technique to estimate SSC in intact dovyalis hybrid fruit ( D. abyssinica Warb.…”

Section: Resultsmentioning

confidence: 97%

“…This analytical method has been used to determine internal quality attributes of various fruit, even in small fruit, such as açaí and juçara, jaboticaba and wax jambu fruit . Reflectance optical geometry was used to predict SSC, TA and pH in intact strawberry and in grapes . However, no results can be found regarding the use of NIRS to evaluate dovyalis fruit quality.…”

Section: Introductionmentioning

confidence: 99%

PLS, iPLS, GA‐PLS models for soluble solids content, pH and acidity determination in intact dovyalis fruit using near‐infrared spectroscopy

Assis¹,

Fusco

Costa

et al. 2018

J Sci Food Agric

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“…Zhang et al (2017) studied actual-time detecting and in situ of enzymatic procedure of wheat protein using miniature fiber NIR spectrometer. Xiao et al (2018) reported the use of NIRS to monitor the developmental stage of white and red grapes. Villar et al (2017) have successfully applied sensor system (VIS-NIR) and chemometrics to monitor the cider fermentation procedure.…”

Section: Introductionmentioning

confidence: 99%

Rapid detection model of Bacillus subtilis in solid‐state fermentation of rapeseed meal

Zheng

Jiang

et al. 2019

Journal of Food Safety

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The main objective of this research was to monitor variety in the content of Bacillus subtilis 10160 during solid‐state fermentation (SSF) of rapeseed meal by near infrared (NIR) spectroscopy and chemometrics. Observations showed the coefficient of determination (R2) value was 0.9401 and root‐mean‐square deviation was 0.639 log (CFU/g) for viable cell content by synergies between four intervals 5,203.003–5,600.267, 5,604.124–6,001.388, 6,807.487–7,204.751 and 8,411.972–8,809.235 cm−1. The determination coefficient of prediction (Rp2) and root‐mean‐square deviation of prediction in an external validation set could reach 0.9532 and 0.628 log (CFU/g) by the viable B. subtilis model. These findings suggest that rapid detection of B. subtilis in SSF is achieved by the combination of synergy interval partial least squares and NIR spectroscopy.

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Quality assessment and discrimination of intact white and red grapes from Vitis vinifera L. at five ripening stages by visible and near-infrared spectroscopy

Cited by 49 publications

References 25 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

PLS, iPLS, GA‐PLS models for soluble solids content, pH and acidity determination in intact dovyalis fruit using near‐infrared spectroscopy

Rapid detection model of Bacillus subtilis in solid‐state fermentation of rapeseed meal

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