Università degli studi di Modena e Reggio Emilia

Near Infrared spectroscopy (NIR), in combination with Chemometrics, has been used for many years in diverse scenarios, mostly focused on the classification and quantitation of properties in food, pharmaceutical prepara- tions, artwork material, etc. This success has been possible due to their desirable properties: fast, reliable (under certain conditions), non-destructive, easy to implement from a hardware perspective, and able to create robust and transferable multivariate models. For some years now, another modality has been gaining the attention of NIR users, especially in the Food sector. That is the plausibility of using NIR in the hyperspectral (HSI) domain. This adds to the previously mentioned abilities, the benefit of scanning the whole surface of samples, acquiring much richer spatial infor- mation and, therefore, assuring the quality of the final product more accurately by including parameters that depend on the surface distribution of certain components. This is especially relevant in heterogeneous samples. While this statement is generally true, there are certain situations where this oversampling feature is not strictly needed, and the problem can be easily solved with a classical NIR spectrophotometer. Besides, NIR-hyperspectral imaging (NIR-HSI), despite the abovementioned advantages, has several drawbacks that must be highlighted as well, like their measuring speed, instability, or price. This manuscript will demonstrate that for certain situations, tuning the focal distance of a NIR spectropho- tometer is a more feasible, reliable, and inexpensive strategy to collect all the needed information of samples with a certain degree of heterogeneity.

Background The study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. Results ROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. Significance This study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS.

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.

In the petrochemical industry, in order to control the final product quality over time and to detect potential plant failures, the amount of lab (off-line) analysis performed every day is very demanding in terms of resources and time. Hence, at/in-line monitoring can be an efficient solution to decrease chemical wastes and operators’ efforts and to perform a fast detection of deviations from normal operative conditions. Moving toward this implementation requires both installation of analytical sensors and the development of models capable to predict in real time the quality parameters of the polymers based on both process and analytical sensors. The primary aim of the current work has been the development of real time monitoring models by advanced chemometric tools for the prediction of a General Purpose PolyStyrene (GPPS) quality property, fusing Near Infrared (NIR) and process sensors data. In the plant considered, in addition to standard process sensors, along the GPPS production line, operating in continuous, two NIR probes are installed in-line. After the arrangement of the available data in different blocks, aiming at studying the specific contribution of the two types of sensors and of the main phases of the process, Multiblock-PLS (MB-PLS) method was employed to fuse the different blocks and to assess which were the most relevant sensors and plant phases for the prediction of the two quality parameters. Good prediction performances were achieved, allowing identifying the most significant data blocks for the GPPS quality prediction. Moreover, prediction errors obtained by models computed without considering blocks of data belonging to the final stages of the process were similar to those involving all the available data blocks. Therefore, a good real time assessment of the GPPS quality can be obtained even before the production is completed, which is very promising in view of minimizing the number of off-line laboratory analyses

Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters’ prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself.