## 5 Spatially-Resolved Spectroscopic Technique for Optical Property Measurement
### Quality Evaluation of Horticultural Products
Calibration models were developed for \(\mu_{\text{a}}\) and \(\mu_{\text{s}}^{\prime}\) using PLS, and for their combination using stepwise MLR. The results showed that both \(\mu_{\text{a}}\) and \(\mu_{\text{s}}^{\prime}\) were correlated with firmness and SSC, but \(\mu_{\text{a}}\) achieved higher correlations than \(\mu_{\text{s}}^{\prime}\). While \(\mu_{\text{s}}^{\prime}\) was assumed to be more related to firmness, it was not as effective as \(\mu_{\text{a}}\) for firmness prediction, which may be explained by the fact that the physiological process of the fruit is often accompanied with changes in chemical components. The combined data were found to be the most effective for firmness and SSC predictions with \(r=0.86\) and \(0.75\), respectively. [PERSON] et al. (2017) further investigated other types of the combined data for predicting apple firmness and SSC. Three combined data were formed, including \(\mu_{\text{a}}\)&\(\mu_{\text{s}}^{\prime}\), \(\mu_{\text{a}}\times\mu_{\text{s}}^{\prime}\) and \(\mu_{\text{eff}}\), where \(\mu_{\text{a}}\)&\(\mu_{\text{s}}^{\prime}\) refers to the simple cascading of the \(\mu_{\text{s}}^{\prime}\) and \(\mu_{\text{a}}\) spectra into one spectrum, \(\mu_{\text{a}}\times\mu_{\text{s}}^{\prime}\) is the multiplication of the \(\mu_{\text{s}}^{\prime}\) and \(\mu_{\text{a}}\) wavelength by wavelength, and \(\mu_{\text{eff}}=\left[3\mu_{\text{a}}(\mu_{\text{a}}+\mu_{\text{s}})\right]^ {1/2}\) is the effective attenuation coefficient characterizing the light penetration ability. A pooled set of apples freshly harvested and after storage were collected, and PLS was used for model calibration. The authors also found that \(\mu_{\text{a}}\) was more effective for firmness and SSC predictions than \(\mu_{\text{s}}^{\prime}\), and that the combined data resulted in improved predictions in most cases, compared to individual \(\mu_{\text{a}}\) and \(\mu_{\text{s}}^{\prime}\). Figure 25 shows the best prediction results for firmness and SSC using the combined data. The \(\mu_{\text{a}}\times\mu_{\text{s}}^{\prime}\) gave the best predictions of firmness and SSC for 'Golden Delicious' apples with \(r=0.89\) and \(0.78\), respectively, while for 'Delicious', \(\mu_{\text{eff}}\) and \(\mu_{\text{a}}\)&\(\mu_{\text{s}}^{\prime}\) were the most effective with \(r=0.86\) and \(0.83\) for firmness and SSC, respectively. In another study on peaches, [PERSON] et al. (2017) utilized the combined data, i.e., \(\mu_{\text{a}}\times\mu_{\text{s}}^{\prime}\) and \(\mu_{\text{eff}}\), combined with least squares support vector machine (LS-SVM) modelling, for firmness and SSC prediction and color assessment as well. The best predictions were achieved by \(\mu_{\text{eff}}\) for firmness, skin lightness and flesh lightness with \(r=0.75\), \(0.90\) and \(0.74\), respectively, and by \(\mu_{\text{a}}\times\mu_{\text{s}}^{\prime}\) for SSC with \(r=0.50\). Figure 23: Absorption (**left**) and reduced scattering coefficient (**right**) spectra for apple, peach and tomato samples. Reproduced with permission from ([PERSON] et al., 2015), Copyright Springer, 2015. Recently, [PERSON] et al. [119] reported on evaluation of tomato ripeness based on the \(\mu^{\prime}_{\rm s}\) and \(\mu_{\rm a}\) spectra. Six grades of 'Sun Bright' tomatoes, i.e., 'Green', 'Breaker', 'Turning', 'Pink', 'Light-red' and 'Red', were defined based on the degree of ripeness, and three grades were also formed by combining the two adjacent ripeness grades. It was found that the \(\mu_{\rm a}\) value at the wavelength 675 nm decreased progressively with the increasing ripeness of tomatoes, until it was close to zero for the 'Red' tomatoes. The classification models based on PLS discriminant analysis achieved detection rates of 92.3% by \(\mu_{\rm a}\)&\(\mu^{\prime}_{\rm s}\) for the three-grade classification, and 88.4% by \(\mu_{\rm eff}\) for the six-grade classification.