Building kinetic models for apple crispness to determine the optimal freshness preservation time during shelf life based on spectroscopy

Abstract

Crispness is a vitally significant factor to evaluate apple texture quality. This study explores quantitative prediction and kinetic models to determine the optimal preservation time of apple crispness during shelf life based on spectroscopy. The nonlinear iterative partial least squares algorithm was used to establish a quantitative prediction model based on the wavelength of 450–1000 nm. Kinetic models were developed to determine the preservation time of apple crispness at room and refrigeration temperatures. The results indicate that the determination coefficients of calibration and prediction sets were 0.8939 and 0.9206 respectively, and the root mean square errors of calibration and prediction sets were 0.1254 and 0.1669 kg, respectively. The determination coefficients of the kinetic models at room and refrigeration temperatures were 0.965 and 0.87, respectively. Consequently, the preservation time of the optimal freshness of Fuji apple crispness was five weeks at room temperature and eight weeks at refrigeration temperature. Practical applications This study provides a method for nondestructively and accurately detecting apple crispness via spectroscopy. This study also provides optimal freshness taste of apple crispness and how long apple crispness could retain under the conditions of room and refrigeration temperature during shelf life for customers and academics. The corporate could extend the preservation time according to this study to improve the profile.

Feasibility of nondestructive detection of apple crispness based on spectroscopy and machine vision

“…if there was only one peak in the first compression curve of the TPA texture map, the peak was defined as firmness (Ni et al, 2020).…”

“…Texture is a major factor in the sensory evaluation of apples and many other fresh fruit, and it is critical or important in quality grading and marketing (Bonany et al, 2013;Cliff & Bejaei, 2018). As a prominent texture attribute, crispness is a key driver in consumer preferences of apples (Ni, Meng, Gu, & Hu, 2020), and having a rapid tool to detect apple crispness would be an important asset in the development of high-quality apples. Loss of crispness in apple fruit during preservation reduces the fresh sensation of fruit and thus consumer acceptance.…”

“…The changes in firmness and SSC of apples during the postharvest period were studied by NIR spectroscopy (Giovanelli, Sinelli, Beghi, Guidetti, & Casiraghi, 2014). Moreover, two studies conducted nondestructive detection of apple crispness based on spectroscopy (Ni et al, 2020;Ni, Zhu, Gu, & Hu, 2019). Effective wavelength selection methods combined with a variety of modeling methods were used to conduct nondestructive detection of apple crispness (Ni et al, 2019).…”

“…Effective wavelength selection methods combined with a variety of modeling methods were used to conduct nondestructive detection of apple crispness (Ni et al, 2019). To nondestructively detect apple crispness, a quantitative prediction model based on wavelengths of 450 to 1,000 nm was established by partial least squares (PLS), and a kinetic model was developed to determine the preservation time of the optimal freshness of apple crispness at room temperature and refrigeration temperature (Ni et al, 2020). Although these studies have realized nondestructive detection of apple crispness based on spectroscopy, the spectral information obtained during the detection process contains the information of the fruit peel, and the heterogeneity of fruit peel and flesh parts will interfere with the detection, resulting in limited accuracy of crispness detection.…”

Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

“…Kinetic models were employed to determine the quality of fruits, vegetables, seafoods, and juices during storage due to their ability to reflect changes related to food quality (Derossi, Pilli, & Fiore, 2010;Giannakourou & Taoukis, 2003;Lei, Luo, Sun, & Shen, 2011;van Boekel, 2008). In addition, previous studies built kinetic models for the prediction of the shelf life of fruits (Hui et al, 2013;Ni et al, 2020;Hu, Liu, & He, 2014). The quality characteristics and shelf life of fresh jujube were predicted via the built zero-order kinetic model for vitamin C content (Hu, Cong, Qian, Zhang, & Yong, 2013).…”