Towards Sensory Assessment Classification using Short-Wave NIR Spectroscopy for Orange Cultivars

Abstract

Abstract The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in an objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification for Pakistani cultivars of orange i.e., Blood red, Mosambi, and Succari. The correlation between quality indices i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra is analyzed. Mix cultivar oranges are then classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired by sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55 respectively, on independent test data. For direct fruit classification, the ensemble classifier achieved 81.03% accuracy for 3 class (sweet, mix, and acidic) classification on independent test data. We observed a good correlation between NIR spectra and sensory assessment instead of quality indices. Hence, direct classification is more suitable for orange sweetness classification using NIR spectroscopy than the estimation of quality indices.

Plant phenotyping relevance

オレンジ果実の甘味・品質という植物器官の形質を、短波長NIR分光で非破壊推定・分類する手法を開発および評価しており、表現型取得が研究の中心である。

Code and data availability

The paper's NIR spectral dataset with destructive testing ground truth and sensory labels is not publicly deposited; the authors state it will be shared only upon request from the first author. No public URL or repository is provided.

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