## 3 Spectral Scattering Imaging
### Quality Evaluation for Horticultural Products  
#### Apple  
The apple is one of the most valuable fruits around the world. Firmness and SSC are the two important quality attributes that determine the quality of apples and directly affect consumer acceptance and satisfaction. Firmness describes a sensation of texture in the mouth, which is related to the mechanical and structural properties of fruit tissue, while SSC is a measure of an aggregate amount of sugars (i.e., sucrose, glucose, and fructose) and is related to fruit sweetness. Spectral scattering imaging has been used for assessing firmness and SSC of apple and other fruits. In using multispectral scattering imaging with a mechanical filter wheel of five wavebands (Figure 6d), coupled with neural network modeling, [PERSON] [24] reported the firmness prediction with \(r=0.87\) and a standard error for prediction (SEP) of 5.8 N for 'Delicious' apples. [PERSON] and [PERSON] [50] reported on using a multispectral imaging system with LCTFs (Figure 6c) to acquire scattering images from 'Delicious' apples. After comparing the three distribution functions (i.e., exponential, Gaussian, and Lorentzian functions, each with three parameters), they concluded that the Lorentzian function was the best for characterization of scattering profiles. In a further study, [PERSON] and [PERSON] [53] reported that by using a modified four-parameter Lorentzian distribution (MLD) function for characterizing scattering profiles, the LCTF-based multispectral scattering system achieved firmness predictions with \(r=0.90\) and SEP = 6.4 N for 'Delicious', and \(r=0.90\) and SEP = 6.1 N for 'Golden Delicious'. [PERSON] and [PERSON] [25] further demonstrated that the modified Gompertz function with three or four parameters performed as well as or better than the modified Lorentzian function, in predicting the firmness of 'Golden Delicious'. All the aforementioned studies focused on detection of stationary samples. To achieve online real-time detection, [PERSON] and [PERSON] [49] developed a multi-laser based multispectral scattering imaging prototype for firmness prediction of apples (Figure 6b). Four lasers centered at 680, 880, 905, and 940 nm were fused into a single optic fiber, and the backscattering images generated by the fused lasers were then captured from the apples moving on the belt conveyor. Experimental tests showed the firmness prediction of 'Golden Delicious' and 'Delicious' apples with \(r=0.86\), demonstrating the feasibility of spectral scattering imaging technique for online fruit quality evaluation. However, the system was still too slow to meet the online speed requirement and there exists a great challenge for proper orientation of each fruit for scattering imaging. [PERSON] [48] used hyperspectral scattering imaging to predict firmness for apples. Mean and SD spectra were extracted from hyperspectral scattering images, and neural network modelling combined with PCA were used to develop calibration models, which produced the best results with \(r^{2}=0.76\) and 0.55 and SEP = 6.2 and 6.1 N, for 'Golden Delicious' and 'Delicious', respectively. [PERSON] and [PERSON] [54] used the MLD function for characterizing hyperspectral scattering profiles and achieved the firmness prediction of apples with \(r=0.89\) and SEP = 6.1 N. [PERSON] et al. [65] investigated the textural features of hyperspectral scattering images for prediction of apple firmness. Integrating textural features and wavelet coefficients of mean spectra, coupled with PLS modelling, was more effective than when they were used alone, achieving the predictions with \(r=0.84\)-0.95 and SEP = 5.9-8.7 N.  
In predicting SSC of 'Delicious' apples, [PERSON] [24] employed multispectral scattering imaging with three wavelengths at 880, 905, and 940 nm and achieved the prediction of \(r^{2}=0.59\) and SEP = 0.78 \({}^{\circ}\)Brix. In a later study, [PERSON] [48] reported on the use of hyperspectral scattering imaging for apple SSC prediction.