###### Abstract
Benchtop NIR technology was used to predict the moisture content and oil content of pomace, and good predictive models were found, as reported in [6; 34]. Using only benchtop NIR technology in [33], spectrophotometric models suitable for the rapid and accurate measurement of the oil and water content in olive pomace were developed. The accuracy of the models was 3.7% (\(\pm\)0.5) and 2.5% (\(\pm\)0.5) for oil content in pomace on a wet and dry basis, respectively. A preliminary study [35] was carried out to verify the applicability of portable vis/NIR spectroscopy devices to predict the oil content in pomace during the milling process. Strong predictive models were developed and applied in an online system to monitor oil loss and perform a feed-forward control to improve the process efficiency. The studies mentioned above have demonstrated the feasibility of using near-infrared spectrometry analysis by using a laboratory instrument or a portable instrument to predict water and oil content in pomace. Currently, despite research and application efforts, NIR technology is used in mills as a method of predictive analysis of the oil and water content in pomace using portable or laboratory instruments. Therefore, the operation is not carried out in real time but requires at least 5-10 min to take the sample, place it correctly in the measuring cells of the instrument, and carry out the measurement. Considering the average mass feed rates of olive oil extraction plants, a result obtained after 5-10 min does not allow for the in line implementation of strategies to reduce oil losses in pomace. Furthermore, in large-scale mills that use many extraction lines, they would need to have an adequate number of measurements operators, and this would lead to a further increase in costs. NIR spectroscopy is used in many food processes, such as for the in-line monitoring of (i) the temperature in sausages during industrial heat treatment, (ii) the performance of membrane filtration in the whey protein fractionation process, (iii) the alcohol fermentation process in vinegar production, (iv) the moisture content during the apple chips process, (v) the quality parameters of deep-fried instant noodles, and (vi) food adulteration [36; 37; 38; 39]. In the olive oil chain, NIR technology, albeit with offline tools, in addition to predicting water and oil content in pomace, is used in different applications, such as evaluating the olive ripening level, distinguishing between olive oil categories, and identifying herbicide residues in olives. So far, no NIR technology has been developed to continuously analyze the characteristics of the pomace discharged from the decanter specifically designed to be implemented in line with the olive oil extraction plant. The overall objective of the present study was the development and installation of a novel NIR device in an industrial olive oil extraction plant for in-line monitoring of the pomace discharged from the decanter. The development of a multivariate calibration model was executed for the real-time monitoring of moisture and oil content in pomace. The specific objectives of the present study were to evaluate the accuracy, robustness, and reliability of the spectral response obtained with the NIR sensor for monitoring the moisture and oil content of pomace during the extraction process, to identify the most important wavelengths with a multivariate data analysis, and, finally, to develop multivariate calibration models and apply the model to predict moisture and oil content in the pomace during the process.