Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy

Travers, Sylvia; Bertelsen, Marianne; Petersen, Karen Koefoed; Kucheryavskiy, Sergey

doi:10.1016/j.lwt.2014.04.048

Cited by 62 publications

(29 citation statements)

References 35 publications

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“…Subsequently, a two‐step approach, including exponentially decreasing function (EDF)‐based enforced variable selection and adaptive reweighted sampling (ARS)‐based competitive variable selection, was utilized to obtain the key variables based on regression coefficients. Finally, the variable subset was determined by cross‐validation (CV) according to the lowest root‐mean‐square error cross‐validation (RMSECV) (Travers, Bertelsen, Petersen, & Kucheryavskiy, ).…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…The changes in fruit firmness and SSC are closely associated with ripeness. [1][2][3][4]53,54] The statistical values of physical parameters, SSC, and firmness of pear for each class are presented in Table 1. As shown in this table, mass and size of the samples did not change with maturation, but firmness decreased significantly as fruit gradually ripened.…”

Section: Samples Selectionmentioning

confidence: 99%

“…However, internal quality features such as soluble solids content (SSC), pH, and particularly firmness are very important in the quality evaluation. [1][2][3][4] In addition, as the pear is a non-climacteric fruit, optimum ripening stage is one of the most fundamental aspects that influence on the future of the eating quality of fruit as well as fruit postharvest storage. The method adopted to distinguish the ripening stage of pear is still relying on the people's experience and intuitionistic judgment.…”

Section: Introductionmentioning

confidence: 99%

Application of Vis/SNIR hyperspectral imaging in ripeness classification of pear

Khodabakhshian

Emadi

2017

International Journal of Food Properties

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The objective of this research was to create supervised classification models of pear ripeness with the use of hyperspectral imaging system in the visible and short near infrared (425-1000 nm) regions. Spectra and images of 450 pear samples were studied, which were selected from three ripeness stages (unripe, ripe, and overripe). Three classification algorithms-partial least square-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were compared. Successive projection algorithm (SPA) was used to select optimal wavelengths, and gray level co-occurrence matrix (GLCM) was applied to extract four textural feature variables. The overall results revealed that the best model was PLS-DA, of which the correct classification rate (CCR) was 87.86% with the input consisted of the spectra and texture feature of images. Hence, combining spectral with texture analyses were effective for improving pear ripeness prediction. In addition, the same dataset for ripeness classification got a good performance of firmness prediction by PLS in the regression analysis. In addition, using the same input of ripeness classification to make a study on firmness prediction by partial least square analysis revealed a potential for further research, with correlate coefficient of prediction set r pre of 0.84 and root mean square error of prediction of 0.78 N.

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“…Determination of dry matter can be made either destructively, destroying the fruit material in the process, or through non-destructive techniques such as near-infrared (NIR) spectroscopy based on the interaction properties between tissues and electromagnetic energy (Nicolai et al, 2007). Dry matter has been reliably assessed in this fashion for numerous commodities including apple (McGlone et al, 2003), cherry (Escribano et al, 2017), kiwifruit (McGlone & Kawano, 1998), mango (Anderson et al, 2017), and recently pear (Travers et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Consumer perception of d'Anjou pear classified by dry matter at harvest using near‐infrared spectroscopy

Serra

Goke

Diako

et al. 2019

Int J of Food Sci Tech

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Summary Dry matter is increasingly recognised as a reliable indicator of fruit quality and consumer acceptance for numerous commodities. To establish dry matter as an at‐harvest metric predictive of post‐storage and ripening consumer acceptance in pear, cv. d'Anjou fruits were segregated into low (<13%), moderate (13–15.99%) and high (≥16%) predicted dry matter classifications at harvest using near‐infrared spectroscopy and evaluated by untrained consumer panels following cold storage and ripening. Consumers significantly favoured higher dry matter fruits over lower dry matter fruits in terms of perceived firmness, crunchiness, juiciness, sweetness, flavour and overall liking. Consumers were willing to pay premium prices for higher dry matter fruits at an estimated $0.20/lb above average retail prices. Sorting fruit by dry matter at harvest via near‐infrared spectroscopy may overcome issues of variation in fruit maturity and quality to produce more consistent consumer experiences in pears following post‐storage ripening.

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Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy

Cited by 62 publications

References 35 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

Application of Vis/SNIR hyperspectral imaging in ripeness classification of pear

Consumer perception of d'Anjou pear classified by dry matter at harvest using near‐infrared spectroscopy

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