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Nondestructive detection and grading of flesh translucency in pineapples with visible and near-infrared spectroscopy.

Xu, Sai; Ren, Jinchang; Lu, Huazhong; Wang, Xu; Sun, Xiuxiu; Liang, Xin

Authors

Sai Xu

Jinchang Ren

Huazhong Lu

Xu Wang

Xiuxiu Sun

Xin Liang



Abstract

Rapid, accurate, and nondestructive internal quality detection for large and rough surface fruit, such as translucency in pineapples, is challenging. In this paper, a visible and near infrared (VIS/NIR) spectrum-based platform is proposed for optimized detection of pineapple translucency. The internal quality of three batches of samples harvested at the same maturity but on different dates (early, middle, and mid to late harvest stage) were acquired with different spectral settings: VIS to shortwave NIR(400–1100 nm), NIR (900–1700 nm) and VIS/NIR (400–1700 nm). The pineapple samples were manually cut open and divided into three translucency degrees (no, slight, and heavy), according to marketing standards. The Savitzky Golay (SG) and standard normal variate (SNV) were applied to remove jitter and scattering noise, respectively. The successive projections algorithm, principal component analysis and Euclidean distance were combined for feature extraction and measurement, followed by data modeling using the partial least squares regression and probabilistic neural network (PNN). Data correction, data supplementation, and a combination of these were applied for model updating. Experimental results showed that the optimal solution for pineapple translucency detection was to use 400–1100 nm spectrum with SG, SNV, PNN and data supplementation for model updating. With only the first and second batch of samples used for modeling (validation set accuracy 91.2 %) and updating (validation set accuracy 100 %), the detection accuracy on the third batch samples was 100 %. The proposed methodologies therefore can be used as rapid, nondestructive, and cost-effective tools to detect pineapple translucency to guarantee the marketing of high-quality fruit, which can also guide the postharvest treatment for the pineapple industry to improve market competitiveness as well as to benefit nondestructive quality assessment of other large fruit.

Citation

XU, S., REN, J., LU, H., WANG, X., SUN, X. and LIANG, X. 2022. Nondestructive detection and grading of flesh translucency in pineapples with visible and near-infrared spectroscopy. Postharvest biology and technology [online], 192, article 112029. Available from: https://doi.org/10.1016/j.postharvbio.2022.112029

Journal Article Type Article
Acceptance Date Jul 5, 2022
Online Publication Date Jul 14, 2022
Publication Date Oct 31, 2022
Deposit Date Aug 1, 2022
Publicly Available Date Jan 15, 2024
Journal Postharvest biology and technology
Print ISSN 0925-5214
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 192
Article Number 112029
DOI https://doi.org/10.1016/j.postharvbio.2022.112029
Keywords Pineapple; Translucency; Visible and near infrared spectroscopy; Nondestructive detection
Public URL https://rgu-repository.worktribe.com/output/1713112