Literature DB >> 27309980

Use of Near-Infrared Spectroscopy and Chemometrics for the Nondestructive Identification of Concealed Damage in Raw Almonds (Prunus dulcis).

Cristian Rogel-Castillo, Roger Boulton, Arunwong Opastpongkarn, Guangwei Huang1, Alyson E Mitchell.   

Abstract

Concealed damage (CD) is defined as a brown discoloration of the kernel interior (nutmeat) that appears only after moderate to high heat treatment (e.g., blanching, drying, roasting, etc.). Raw almonds with CD have no visible defects before heat treatment. Currently, there are no screening methods available for detecting CD in raw almonds. Herein, the feasibility of using near-infrared (NIR) spectroscopy between 1125 and 2153 nm for the detection of CD in almonds is demonstrated. Almond kernels with CD have less NIR absorbance in the region related with oil, protein, and carbohydrates. With the use of partial least squares discriminant analysis (PLS-DA) and selection of specific wavelengths, three classification models were developed. The calibration models have false-positive and false-negative error rates ranging between 12.4 and 16.1% and between 10.6 and 17.2%, respectively. The percent error rates ranged between 8.2 and 9.2%. Second-derivative preprocessing of the selected wavelength resulted in the most robust predictive model.

Entities:  

Keywords:  Prunus dulcis; almond; concealed damage; discriminant analysis; near-infrared spectroscopy; partial least squares regression

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Year:  2016        PMID: 27309980     DOI: 10.1021/acs.jafc.6b01828

Source DB:  PubMed          Journal:  J Agric Food Chem        ISSN: 0021-8561            Impact factor:   5.279


  2 in total

Review 1.  Recent advances and effective strategies in the discovery and applications of natural products.

Authors:  Jing Xie; Ai-Hua Zhang; Hui Sun; Guang-Li Yan; Xi-Jun Wang
Journal:  RSC Adv       Date:  2018-01-03       Impact factor: 4.036

2.  Application of hyperspectral imaging technology for rapid identification of Ruditapes philippinarum contaminated by heavy metals.

Authors:  Yao Liu; Fu Qiao; Shuwen Wang; Runtao Wang; Lele Xu
Journal:  RSC Adv       Date:  2021-11-15       Impact factor: 3.361

  2 in total

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