Abstract
Firmness changes in Nanguo pears under different freezing/thawing conditions have been characterized by hyperspectral imaging (HSI). Four different freezing/thawing conditions (the critical temperatures, numbers of cycles, holding time and cooling rates) were set in this experiment. Four different pretreatment methods were used: multivariate scattering correction (MSC), standard normal variate (SNV), Savitzky-Golay standard normal variate (S-G-SNV) and Savitzky-Golay multiplicative scattering correction (S-G-MSC). Combined with competitive adaptive reweighted sampling (CARS) to identify characteristic wavelengths, firmness prediction models of Nanguo pears under different freezing/thawing conditions were established by partial least squares (PLS) regression. The performance of the firmness model was analyzed quantitatively by the correlation coefficient (R), the root mean square error of calibration (RMSEC), the root mean square error of prediction (RMSEP) and the root mean square error of cross validation (RMSECV). The results showed that the MSC-PLS model has the highest accuracy at different cooling rates and holding times; the correlation coefficients of the calibration set (Rc) were 0.899 and 0.927, respectively, and the correlation coefficients of the validation set (Rp) were 0.911 and 0.948, respectively. The accuracy of the SNV-PLS model was the highest at different numbers of cycles, and the Rc and the Rp were 0.861 and 0.848, respectively. The RMSEC was 65.189, and the RMSEP was 65.404. The accuracy of the S-G-SNV-PLS model was the highest at different critical temperatures, with Rc and Rp values of 0.854 and 0.819, respectively, and RMSEC and RMSEP values of 74.567 and 79.158, respectively.
Highlights
In the process of fruit freezing and refrigeration, the existence of ice crystals will cause mechanical damage to the microstructures of fruit, such as the cell membrane and cell wall [1,2], which directly determines the fresh-keeping state during fruit storage
88 Nanguo pear samples were used as calibration set samples
32 Nanguo pear samples, which were numbers 23 to 30 from the fresh group, numbers 53 to 60 from the one cycle group, numbers 83 to 90 from the two cycles group and numbers 113 to 120 from the three cycles group, were used as validation set samples to carry out the modeling and comparative analysis
Summary
In the process of fruit freezing and refrigeration, the existence of ice crystals will cause mechanical damage to the microstructures of fruit, such as the cell membrane and cell wall [1,2], which directly determines the fresh-keeping state during fruit storage. Most of the research has focused on the analysis of fruit quality during postharvest storage [3], but there is a lack of nondestructive and rapid detection methods. Hyperspectral reflectance imaging mainly utilizes the spectral and image information acquired by light reflection after absorption by imaging through fruit and vegetable tissues [4]. Depending on the obtained high-resolution spatial information and spectral information, the characteristic wavelengths of fruits and vegetables can be extracted, and the comprehensive quality can be quickly and effectively detected. Hyperspectral applications are mainly focused on hyperspectral remote sensing [5,6,7] and food quality assessment [8,9,10]. Hyperspectral technology has been successfully applied to meat
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
