# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 3 Hyper- and Multispectral Imaging
### Chemometric Models  
Hyper- and multispectral imaging generates reflectance values from each waveband. Nevertheless, it is indirectly applicable. Chemometrics is an analytical approach originally  
Figure 4: Hyper- and multispectral acquisition methods: (**a**) point scan; (**b**) line scan; and (**c**) area scan. derived from statistical and mathematical concepts [67]. This analytical step is favorably employed by researchers to develop prediction models based on reflectance values. Chemometric techniques have been intensively carried out to predict the desired objectives within agri-food production lines, e.g., chemical content, food sensory, and plant monitoring status. Moreover, chemometrics also promotes simple presentation by reducing the dimension and complexity of large datasets, classifies samples, and enables data modeling, with robust accuracy [68]. Principal component analysis (PCA) has been successfully applied by many researchers as a tool to analyze spectral data. Since spectral data consist of numerical reflectance values for each waveband, dimensional reduction can be performed to display the information clearly in a two- or three-dimensional plot (called PCs). Although PCA simply presents variances in a scatter plot, it does not lose the information. PCA is also an acceptable method for observing outliers and constructing models. For instance, PCA clearly showed that adulterant materials had different reflectance values compared to fresh-cut vegetables [10]. Fresh-cut vegetables had a similar reflectance value, clustered together as a group, and were separated from adulterant materials. In another study, PCA was used to illustrate the contrast maps of chemical contents in different treated plants. In contrast, [PERSON] et al. [69] reported that PCA had a lower coefficient of determination (R\({}^{2}\) = 0.69) in predicting total volatile basic nitrogen in pork. This was caused by the fact that PCA only obtained a single waveband (460 nm) to build the TVB-N prediction model. At present, due to the high dimensionality of spectral data acquired using HSI/MSI, many researchers apply wavelength selection before performing chemometric analysis. The high dimensionality of HSI/MSI spectral data can be further reduced to a low dimension through feature extraction and feature selection. Feature extraction utilizes the original data, while feature selection requires class marking to choose the representative wavelength. However, wavelength extraction can remove intrinsic features [70]. In optical spectroscopy, outliers may occur during the data acquisition process. This phenomenon may be present due to the environment and operator errors [71; 72]. Negatively, outlier samples can decrease the accuracy of the built predictive model. Therefore, outliers should be deselected before employing chemometrics. Principal component analysis (PCA) can detect such outliers. In a study by [PERSON] et al. [65], two-dimensional PC plots were used to figure out outliers. Since PCA can simply display clusters based on their similarity, sample outliers can easily be detected. Similarly, the effectiveness of PCA in tracing outliers had been proven previously [73]. To date, PLS-R has been intensively developed to establish predictive models of targeted components based on spectral features [74]. A latent variable--typically abbreviated as LV--is considered an important parameter to investigate the linear relatedness of both spectrum matrices and reference values. High accuracy of PLS-R depends on the number of chosen LV values, which is determined based on the lowest point of cross-validation root mean squared error [75]. Nevertheless, the accuracy of PLS-R relates to the validity of the reference matrices obtained using destructive methods. Generally, PLS-R itself is considered to be a linear regression and is mathematically described as shown below (Equation (3)).