Abstract
Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R2c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R2p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.
Highlights
Cucurbitaceae, often referred to as cucurbits, is the largest tropical vegetable group and is widely cultivated in various parts of the world
The quality of agricultural goods is not fully assessable based on visuals, as this does not account for internal characteristics, including soluble solids content (SSC) and water content
The multi-product samples showed an extensive range in both attributes, and the distribution peak between 3.17 – 8.48 %Brix (SSC) and 89.80–95.76 % was due to the broad variation in the samples used
Summary
Cucurbitaceae, often referred to as cucurbits, is the largest tropical vegetable group and is widely cultivated in various parts of the world. The quality of agricultural goods is not fully assessable based on visuals, as this does not account for internal characteristics, including soluble solids content (SSC) and water content. These two attributes are very important aspects of consumers' acceptance of agricultural goods because both are associated with taste (Park et al, 2018). For SSC, the standard method of measurement is the refractometric approach (Hadiwijaya et al, 2020b). This technique computes the refractive index of the juice sample using a refractometer. The methods used to assess these food product properties seem time-consuming, and destructive, as products subjected to analyses are no longer sellable
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