Non-destructive measurement of internal quality of apple fruit by a contactless NIR spectrometer with genetic algorithm model optimization

Abstract

Abstract Spectrometric methods based on near infrared radiation (NIR) are commonly used effectively in the agricultural and food industry. However, these methods still face limitations whereby meeting requirements for application such as nondestructive quality testing of large fruits and automated sorting and grading is still a challenge. A Fourier transform (FT)-NIR spectrometer (emission head, EH mode of Matrix-F) that simulates on-line sample scanning (contactless, large sample size (100\u202fmm)) was used to predict internal properties of apple fruit. The EH was compared to laboratory multipurpose analyzer (MPA) FT-NIR spectrometer using two contact-sample presentation modes with relatively smaller sample size (≤22\u202fmm); namely, the integrating sphere (IS) and the solid probe (SP). Three apple cultivars (Golden Delicious, Granny Smith and Royal Gala) sourced from two retail stores (in Stellenbosch, South Africa) were used to constitute variability in the sample set. Partial least squares regression (PLSR) prediction models for internal quality (total soluble solids (TSS) and titratable acidity (TA)) were developed and validated on external test samples in various scenarios. Genetic algorithm (GA) based optimization of PLS models was used to produce optimal models prior to instrumental comparison. Model optimization using GA improved performance by a margin of 30% of the original root mean square error of cross validation for the contactless system bringing it closer to the performance of models from the MPA. The EH s performance makes it an attractive option for achieving on-line application of NIR spectroscopy for sorting apples based on internal quality.