Abstract
Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200–1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively.
Highlights
The marketing can be managed by enhancing cost-effective and non-destructive quality control systems in food industries
The evaluation of purple berry fruit was conducted by Maniwara et al [19] using NIR spectroscopy; the results indicated a significant relationship between the estimated and the actual values (0.84, 0.91, and 0.99 for soluble solid content (SSC), titratable acidity (TA), and pulp content (PC), respectively)
The present paper proposed the prediction of total soluble solids (TSS) and BrimA of Gala cultivar apples using Artificial Neural Network-Imperialist Competitive Algorithm (ANN-imperialist competitive algorithm (ICA)) and partial least squares regression (PLSR) based on spectral data related to key wavelengths
Summary
The marketing can be managed by enhancing cost-effective and non-destructive quality control systems in food industries. The NIR spectroscopy has been successfully used to measure the physicochemical features of food and agricultural products nondestructively [1,2,3,4]. The internal properties of various fresh fruits have been successfully evaluated for several decades using NIR spectroscopy [5,6,7,8,9,10,11,12]. The results of a variety of spectroscopic and chemometric techniques have proven that NIR spectroscopy alone is quite effective for determining the physical and chemical properties of several fruits [13,14,15]. The applications of Vis/NIR spectroscopy were reviewed by Li et al [18] for quality evaluation of oilseeds. The ability of spectroscopy to identify the geographical origin of oilseeds and edible oils was studied. Maniwara et al [19]
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