## 4 E-Nose: Principles and Main Agri-Food Applications
Normally, clustering actions are carried out, by using both supervised and unsupervised approaches, depending on the specific tasks demanded. Under this light, methods such as Support Vector Machines (SVM) or Artificial Neural Networks (ANN), if not more complex algorithms such as the ones applied for Deep Learning (DL), are to be considered among the most popular when it comes to use within E-nose systems. E-nose holds many advantages with respect to traditional sensory analysis: it is more accurate and reliable, it is less time consuming and it is less influenced by environmental factors. Furthermore, if compared with analytical methods, it does not require strong expertise to be used and interpreted as gas chromatography does. As such, the application of the E-nose in various fields, including the food and pharmaceutical industries, as well as in health and well-being, is expanding rapidly [54]. As the olfactory fingerprint provides important information regarding the characteristics, origin and processing methods of food, the E-nose systems have been widely studied and used in the agri-food sector (Table 1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Application** & **Sensor Arrays** & \begin{tabular}{c} **Chemometrics** \\ **Approach** \\ \end{tabular} & \
\begin{tabular}{c} **Classical Methods for** \\ **Comparison** \\ \end{tabular} & **Reference** \\ \hline Discrimination of two varieties of & & & & & \\ galangal (_Alpiria officinarum_) & MOS & PCA & GC-MS & [55] \\ \hline Dogfruit (_Pithecellobium jiririsa_) and & & & & \\ stink bean (_Parkia specios_) ripening & Chemical sensors & PCA, HCA & GC-FID & [56] \\ \hline Discrimination of three varieties of & & & & \\ garlic (_Allium sativum L._) & MOS & PCA & GC-MS & [57] \\ \hline Discrimination between mango (cv & & & & \\ Chokanan) ripening stages & & & & & \\ quartz crystal) & PCA, HCA & GC-FID & [58] \\ \hline Identification of five _Piperno_ L. & MOS & PCA & HS-SPME GC-MS & [59] \\ \hline Identification of three mango & & & & \\ varieties (_Manguifera indica L._) and & MOS & DFS & GC & [60] \\ ripening stage & & & & \\ \hline Discrimination of two tomato & & & & \\ (_Lyocpersicum esculent_) ripening & MOS & PCA, LDA and & & \\ stages (i.e., green stages and ripe) & & DFA & & \\ \hline Discrimination of eight varieties of & & & & \\ apricot (_Prumus armeniaca_) & MOS & PCA and FDA & & \\ \hline Monitoring volatile constituents of & & & & \\ cocoa (_Thebroma cacao_) during the & MOS & PCA & HS GC-MS & [63] \\ refining process & & & & \\ \hline Evaluation of freshness of broccoli & & & & \\ during storage (_Brassica oleracea_ L.) & MOS & PCA, CDA & HS GC-MS, FTIR & [64] \\ \hline Characterization of volatile & & & & \\ compounds in soybean seeds & MOS & PCA & HS-SPME GC-MS & [65] \\ (_Glycine max_ L.) & & & & \\ \hline Monitoring the hardness of litch & & & & \\ under different storage conditions & MOS & LDA, CCA, & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{**Freshness evaluation, flavor and aroma**} & MS based & & & \\  & (piezoelectric & & & \\  & quartz crystal) & PCA, MLR & & \\  & & quartz crystal) & & \\ \hline Monitoring of pineapple (_Annas_ & & & & \\ _comosus_) shelf life during storage at & MOS & PCA, CA & & [68] \\ different temperatures & & & & \\ \hline \multirow{2}{*}{Aroma development during ripening and storage of apricots} & MOS & PCA & & \\  & & & & \\ \hline Evaluation of maturity and shelf life & & & & \\ of tomatoes (_Lyocpersicum esculentum_) & MOS & PCA, LDA, PLS & Firmness & [70] \\ \hline Apple and orange post-harvest & & & & \\ quality, e.g., detection of defects of apples and oranges & MOS & PCA, PLS-DA &
\begin{tabular}{c} Amount of mealiness \\ and skin damage \\ \end{tabular} & [71] \\ \hline Classification of 90 different & & & & \\ blended and roasted coffee samples & MOS & DFA, MANOVA & & [72] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main applications of the electronic nose in the agro-food sector.