Rapid and nondestructive monitoring for the quality of Jinhua dry-cured ham using hyperspectral imaging and chromometer

Ni, Chendie; Liu, Hai; Liu, Qiang; Sun, Ye; Pan, Leiqing; Fisk, Ian Denis; Liu, Yuan

Abstract

The purpose of this study was to build a rapid and nondestructive method to evaluate the quality of Jinhua dry-cured ham (JHH). Hyperspectral imaging (HSI) in the spectral range of 400–1,000 nm was acquired to build a quantitative model to monitor the quality of JHH, coupled with characteristic color indicators that highly correlated to the quality of JHH to simply data process. The feature wavelength of spectral data was selected by the successive projection algorithm. Quantitative regression models based on four spectral preprocessing methods were established to predict color indicators. And color indicators can reflect the difference of quality attributes of JHH, especially ΔL* and ΔE*. For Jinhua second-grade ham, effective models (δL* and δE*) had optimal (Formula presented.) of 0.920, 0.938 and root mean square error of prediction of 1.02, 1.12. Therefore, this rapid and nondestructive method for quality evaluation of JHH dry-cured ham has the potential to apply to modernization processing of manufacture. Practical Applications: For the modern agri-food industry, quality is one of the most important concerns and a rapid, nondestructive, standard, and objective technology is needed to be used to monitor the quality of dry-cured ham efficiently. Hyperspectral imaging (HSI) system is a nondestructive technique to evaluate the color of dry-cured hams hence supports the quality evaluation of them. The purpose of this study was to build a quantitative model by HSI to monitor the quality of Jinhua dry-cured ham according to its color indicators.

Citation

Ni, C., Liu, H., Liu, Q., Sun, Y., Pan, L., Fisk, I. D., & Liu, Y. (2020). Rapid and nondestructive monitoring for the quality of Jinhua dry-cured ham using hyperspectral imaging and chromometer. Journal of Food Process Engineering, 43(8), Article e13443. https://doi.org/10.1111/jfpe.13443

• Journal Article Type: Article
• Acceptance Date: Apr 23, 2020
• Online Publication Date: May 25, 2020
• Publication Date: 2020-08
• Deposit Date: Mar 29, 2021
• Journal: Journal of Food Process Engineering
• Print ISSN: 0145-8876
• Electronic ISSN: 1745-4530
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 43
• Issue: 8
• Article Number: e13443
• DOI: https://doi.org/10.1111/jfpe.13443
• Keywords: Food Science; General Chemical Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/4542197
• Publisher URL: https://onlinelibrary.wiley.com/doi/10.1111/jfpe.13443