Figure 4.Spectra for two different levels of carambola SSC measured through (a) Reflectance (b) Interactance.Carambola with lower SSC value exhibited a steeper spectral absorbance curve compared with those with higher SSC (Figure 4). SSC (in ��Brix) is the percentage of sugars and other soluble solids in water, therefore, a steeper absorbance curve refers to higher water content per aqueous fruit sample volume (juice). Spectral steepness is directly associated with the absorbance curve linearity; thus, allowing SSC to be evaluated quantitatively through a technique called spectral linearisation. Spectral linearisation is defined by the value of the linear coefficient of determination, R2 generated from each spectrum. For instance, the linearity of reflectance spectrum increased from 0.

0962 to 0.2245 with increased SSC from 5.8�� Brix to 9.1�� Brix.Equations (1) and (2) explain the relationship between spectral linearity obtained from the calibration data set and carambola SSC through reflectance and interactance spectra, respectively. This step evaluates the ability of the developed algorithms in producing consistent measurement accuracy levels. The interactance technique produces significantly higher linear coefficient of determination (R2 = 0.769) and lower root mean square of error (RMSEC = 0.422�� Brix) compared with the reflectance technique (R2 = 0.614; RMSEC = 0.545�� Brix):SSC(B��rix)=5.05+17.2(R2940?1025)(1)SSC(B��rix)=2.66+80.9(R2940?1025)(2)The relationship between the predicted and actual carambola SSC via the interactance technique is illustrated in Figure 5.

The interactance measurement technique sustains high accuracy levels in predicting carambola SSC with R2= 0.724 and root mean square error of prediction (RMSEP) = 0.461�� Brix, whereas the reflectance technique produces poor prediction accuracy with R2 = 0.459; RMSEP = 0.645�� Brix.Figure 5.Prediction of carambola SSC through interactance spectral linearisation.In the application of interactance spectral linearisation for carambola SSC measurement, the technique significantly improved the NIR ability to quantifycarambola SSC. The improvement was from the developed high accuracy prediction model compared with those conducted through MLR, an established statistical method for spectroscopy analysis. Table 2 shows the two other sets of results which were obtained using the MLR technique and also MLR technique GSK-3 with first derivative and Savitzky-Golay smoothing technique on visible and NIR spectroscopy data. Data are usually derivatized to remove background noise from spectra, for example specular light reflection [16]. Besides, Savitzky-Golay smoothing is also one of the methods often used to eliminate noises from spectra [17].