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    Non-destructive prediction of pH and total soluble solids of lime [Citrus × aurantifolia (Cristm.) Swinge] by visible and near-infrared spectroscopy
    (2017-11-25)
    Huong, H. T.
    ;
    Teerachaichayut, S.
    The non-destructive visible and near-infrared spectroscopy (Vis/NIRS) technique is well suited for evaluating various internal quality indices of fruits quickly and accurately. The objective of this study was to evaluate the relationships between Vis/NIR measurements and the internal quality indices of lime, including pH and total soluble solids (TSS, °Brix). For this experiment, reflectance measurement in the 400-2500 nm range was done on 140 samples for pH and 117 samples for TSS. Partial least square (PLS) regression was used to establish the calibration models. First-order derivative and multiplicative scatter correction spectral pretreatments were used to develop calibration models for pH and TSS, respectively. The correlation coefficient (R) and the root mean square error of prediction (RMSEP) from the calibration model for pH were 0.95 and 0.06. The corresponding values for TSS were 0.81 and 0.24 °Brix, respectively. The results showed that Vis/NIRS measurements in the spectral range 400-2500 nm could be used to access pH and TSS of lime.
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    Non-destructive prediction of moisture content of lime (Citrus aurantifolia Swingle 'Paan') by multiple regression analysis of its electrical and physical properties
    (2017-03-21)
    Huong, H. T.
    ;
    Teerachaichayut, S.
    Large quantity of juice is an important index of lime quality that consumers seek for. Therefore, a non-destructive technique for prediction of lime juice quantity is needed. In this study, moisture content (MC) of lime which is an indicator of its juice quantity was predicted by multiple regression analysis of its electrical properties -capacitance (C), inductance (L) and impedance (Z) at various frequencies (0.012, 0.05, 0.1, 0.2, 5, 10, 20, 50, 100 and 200 kHz) - and physical parameters - weight and geometric mean diameter (GMD). Samples (n=82) were divided into a calibration set (n=55) and a prediction set (n=27). A calibration model for moisture content of lime was established and cross-validated by partial least squares regression (PLSR). Prediction results achieved a coefficient of determination (R2) of 0.934 and a root mean square error of prediction (RMSEP) of 1.822% wet basic, demonstrating that this technique has a real potential for development into a practical non-destructive lime screening method.