Is this melon sweet? A quantitative classification for near-infrared spectroscopy

Melons are nutritious, healthy, and one of the most eatable summer fruits in South Asia, especially in Pakistan. A melon is delicious if it is sweet, however, the gauge of its sweetness depends on the individual taste buds. In this paper, a direct sweetness classifier is proposed as a quantitative measure, to predict the sweetness of melon as opposed to indirect measure of soluble solid content (SSC/?Brix) based thresholding for near-infrared (NIR) spectroscopy. To provide guidance for fruit sweetness classification, sensory test was conducted, and sweetness standards were established as; very sweet (with ?Brix over 10), sweet (with ?Brix between 7 and 10), and flat (with ?Brix below 7) class. NIR spectral data obtained using F-750 produce quality meter (310?1100 nm) was analyzed to build SSC prediction model and direct sweetness classification model. The best SSC model was obtained using multiple linear regression on second derivative of spectral data (for wavelength range 729?975 nm) with correlation coefficient = 0.93, and root mean square error = 1.63 on test samples. Sweetness of test samples were obtained using ?Brix thresholding with an accuracy of 55.45% for three classes. The best direct sweetness classifier was obtained using K nearest neighbor (KNN) on second derivative of spectral data (for wavelength range 729?975 nm) with an accuracy of 70.3% for three classes on test samples. It was further observed that classification accuracy for sweet and flat melon can be improved by combining sweet and very sweet class samples into one ?satisfactory? class (with ?Brix over 7). For ?Brix thresholding-based classification the accuracy was improved to 80.2% and for KNN based direct sweetness classification the accuracy was improved to 88.12%. Extensive evaluation validates our argument that modeling a direct sweetness classifier is a better approach as compared to ?Brix based thresholding for sweetness classification using NIR spectroscopy.