# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 5 Discussion  
To date, the use of spectroscopy imaging techniques has a wide range in the agricultural field. In general, we found that hyperspectral imaging is more commonly applied than multispectral imaging. The reason is perhaps that HSI can provide high-resolution data compared to MSI. In food safety and quality assessment, [PERSON] et al. [15] had come to similar conclusions. Nevertheless, an MSI system can perform snapshot-based imaging and be more practical, as shown in [32]. Based on our literature review, it can be seen that the use of spectroscopy imaging techniques ranges from seed viability to crop quality evaluation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Plants** & **Instrument** & **Interest** & **Wavebands (nm)** & **Model** & **R\({}^{2}\)** & **RMSE** & **Accuracy (\%)** & **Ref.** \\ \hline Cucumber (_Circum satives_) & HSI (ImSpector VITE, Spectra Imaging, Ltd, Oulu, Finland) & Chlorophyll & 950–1650 & Principal component analysis (PCA) & N/A * & N/A * & N/A & [118] \\  & & & & Multi-linear regression & N/A & N/A & N/A & \\ Pepper (_Captsium_) & HSI (ImSpector V10E, Spectral Imaging Ltd, Oulu, Finland) & Total nitrogen distribution & 380–1000 & Partial least squared regression (PLS-R) & 0.59–0.99 & 0.02–0.47 & N/A & [117] \\ Tea (_Camilia sinensis L_) & HSI (ImSpector N10E, Oulu, Finland) & Nitrogen content & 400–1000 & Partial least squared discriminant analysis (PLS-DA) and least squared support vector machine (LS-SVM) & N/A & N/A & N/A & [119] \\  & & & & Partial least squared regression (PLS-R) & \(\geq\)0.90 & 0.21 & N/A & \\ \hline \hline \end{tabular}
* N/A: not available.  
\end{table} 
Table 6: Recent publications related to application of HSI/MSI to assess nitrogen content. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Plants** & **Instrument** & **Interest** & **Wavebands (nm)** & **Model** & **R\({}^{2}\)** & **RMSE** & **Accuracy (\%)** & **Ref.** \\ \hline Tomato (_Circum satives_) & HSI (ImSpector V10E, Spectral Imaging Ltd, Oulu, Finland) & Cadmium residue & 400–1000 & Wavelet transform and least square support vector machine regression & 0.73–0.94 & 0.15–0.38 & N/A * & [121] \\ Tobacco (_Circum satives_) & HSI (ImSpector V10E, Spectral Imaging Ltd, Oulu, Finland) & Lead discrimination & 400–1000 & Partial least squared discriminant analysis (PLS-DA) & N/A & N/A & 45–50 & [122] \\  & & & & Least squared support vector machine (LS-SVM) & N/A & N/A & \(\geq\)98.33 & \\ \hline \hline \end{tabular}
* N/A: not available.  
\end{table} 
Table 7: Recent publications using HSI/MSI to detect heavy metal residues. Additionally, the effects of abiotic stresses can be easily detected using HSI and MSI, which are normally equipped with several chemometrics approaches. In contrast, investigation on heat stress is still limited. This is prominently caused by heat stress that only appears in several countries, particularly tropical countries. However, in fact, HSI and MSI can distinguish biological stresses and quantify nutrient distribution in leaf. Regarding food safety, heavy metal residues within plant matrixes, such as lead (Pb) and cadmium (Cd), have also been observed using these techniques. Such investigations have been carried out due to the high toxicity of heavy metal residues to the human body. Nevertheless, these studies showed low performance and accuracy. This is perhaps due to the fact that not every heavy metal exhibits a distinct spectral response [123]. As shown in the tables above, various chemometric models have been built using HSI/MSI data. In addition, these models were proposed to predict, classify, and discriminate the spectrum and/or image features acquired by using HSI/MSI. According to our literature review, regression techniques are widely utilized. In Section 3, we offer a brief introduction about PLS-R. Nevertheless, other regression techniques mentioned above are also able to predict chemical compounds in plants with good satisfactory results.