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Tanzilli D, Strani L, Bonacini F, Ferrando A, Cocchi M, Durante C. Implementing multiblock techniques in a full-scale plant scenario: On-line prediction of quality parameters in a continuous process for different acrylonitrile butadiene styrene (ABS) products. Anal Chim Acta 2024; 1316:342851. [PMID: 38969408 DOI: 10.1016/j.aca.2024.342851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/05/2024] [Accepted: 06/07/2024] [Indexed: 07/07/2024]
Abstract
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.
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Affiliation(s)
- Daniele Tanzilli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy; Centre National de la Recherche Scientifique (CNRS), Laboratoire de Spectroscopie pour les Interactions, la Réactivitè et l'Environnement (LASIRE), Cité Scientifique, University Lille, F-59000, Lille, France
| | - Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy.
| | - Francesco Bonacini
- Research Center, Versalis (ENI) S.p.A., Via Taliercio 14, 46100, Mantova, Italy
| | - Angelo Ferrando
- Research Center, Versalis (ENI) S.p.A., Via Taliercio 14, 46100, Mantova, Italy
| | - Marina Cocchi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy
| | - Caterina Durante
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy
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Tanzilli D, D'Alessandro A, Tamelli S, Durante C, Cocchi M, Strani L. A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics. Foods 2023; 12:foods12081679. [PMID: 37107474 PMCID: PMC10137520 DOI: 10.3390/foods12081679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/10/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
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.
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Affiliation(s)
- Daniele Tanzilli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
- Université de Lille, CNRS, LASIRE (UMR 8516), Laboratoire Avancé de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, 59000 Lille, France
| | - Alessandro D'Alessandro
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Samuele Tamelli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Caterina Durante
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Marina Cocchi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
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Šašić S, Veriotti T, Kotecki T, Austin S. Comparing the predictions by NIR spectroscopy based multivariate models for distillation fractions of crude oils by F-test. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 286:122023. [PMID: 36323088 DOI: 10.1016/j.saa.2022.122023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/10/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
The whole range of distillation fractions in industrially relevant crude oil samples is predicted by using two multivariate models based on near-infrared (NIR) spectra. The first versions of the models as well as the respective model updates are considered, with the updates largely aimed at expanding the models. The prediction results are compared across all the fractions and F-test is used to critically compare the performance of the models and the effectiveness of the limited updates. The results suggest that both multivariate methods perform very comparably, and the updates do not lead to statistically significant changes, which differs from what one could conclude from the nominal prediction errors. The near-equivalency of the prediction accuracy of the updated models is additionally illustrated by perusing predictions of a number of batches from one sour and one sweet crude arriving at the refinery during a four month period.
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Affiliation(s)
- Slobodan Šašić
- bp, Global Innovation Analytics, 150 West Warrenville Rd., Naperville, IL 60563, USA.
| | - Tincuta Veriotti
- bp, Global Innovation Analytics, 150 West Warrenville Rd., Naperville, IL 60563, USA
| | - Todd Kotecki
- bp, Whiting Refinery, 2815 Indianapolis Blvd., Whiting, IN 46394, USA
| | - Stacy Austin
- bp, Whiting Refinery, 2815 Indianapolis Blvd., Whiting, IN 46394, USA
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Detection of bacterial spoilage during wine alcoholic fermentation using ATR-MIR and MCR-ALS. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Process Monitoring and Characterization for Extraction of Herbal Medicines Based on Proton (1H) Nuclear Magnetic Resonance Spectroscopy and Multivariate Batch Modeling: a Case Study. J Pharm Innov 2022. [DOI: 10.1007/s12247-022-09629-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Strani L, Mantovani E, Bonacini F, Marini F, Cocchi M. Fusing NIR and Process Sensors Data for Polymer Production Monitoring. Front Chem 2021; 9:748723. [PMID: 34746093 PMCID: PMC8569376 DOI: 10.3389/fchem.2021.748723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022] Open
Abstract
Process analytical technology and multivariate process monitoring are nowadays the most effective approaches to achieve real-time quality monitoring/control in production. However, their use is not yet a common practice, and industries benefit much less than they could from the outcome of the hundreds of sensors that constantly monitor production in industrial plants. The huge amount of sensor data collected are still mostly used to produce univariate control charts, monitoring one compartment at a time, and the product quality variables are generally used to monitor production, despite their low frequency (offline measurements at analytical laboratory), which is not suitable for real-time monitoring. On the contrary, it would be extremely advantageous to benefit from predictive models that, based on online sensors, will be able to return quality parameters in real time. As a matter of fact, the plant setup influences the product quality, and process sensors (flow meters, thermocouples, etc.) implicitly register process variability, correlation trends, drift, etc. When the available spectroscopic sensors, reflecting chemical composition and structure, consent to monitor the intermediate products, coupling process, and spectroscopic sensor and extracting/fusing information by multivariate analysis from this data would enhance the evaluation of the produced material features allowing production quality to be estimated at a very early stage. The present work, at a pilot plant scale, applied multivariate statistical process control (MSPC) charts, obtained by data fusion of process sensor data and near-infrared (NIR) probes, on a continuous styrene-acrylonitrile (SAN) production process. Furthermore, PLS regression was used for real-time prediction of the Melt Flow Index and percentage of bounded acrylonitrile (%AN). The results show that the MSPC model was able to detect deviations from normal operative conditions, indicating the variables responsible for the deviation, be they spectral or process. Moreover, predictive regression models obtained using the fused data showed better results than models computed using single datasets in terms of both errors of prediction and R2. Thus, the fusion of spectra and process data improved the real-time monitoring, allowing an easier visualization of the process ongoing, a faster understanding of possible faults, and real-time assessment of the final product quality.
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Affiliation(s)
- Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | | | | | - Federico Marini
- Department of Chemistry, University of Roma La Sapienza, Roma, Italy
| | - Marina Cocchi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena, Italy
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Foroughi B, Shahrouzi JR, Nemati R. Detection of Gasoline Adulteration Using Modified Distillation Curves and Artificial Neural Network. Chem Eng Technol 2021. [DOI: 10.1002/ceat.202000217] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Babak Foroughi
- Sahand University of Technology Faculty of Chemical Engineering 53318-17634 Sahand New Town, Tabriz Iran
| | - Javad Rahbar Shahrouzi
- Sahand University of Technology Faculty of Chemical Engineering 53318-17634 Sahand New Town, Tabriz Iran
| | - Ramin Nemati
- Sahand University of Technology Faculty of Chemical Engineering 53318-17634 Sahand New Town, Tabriz Iran
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Strani L, Grassi S, Alamprese C, Casiraghi E, Ghiglietti R, Locci F, Pricca N, De Juan A. Effect of physicochemical factors and use of milk powder on milk rennet-coagulation: Process understanding by near infrared spectroscopy and chemometrics. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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de Oliveira RR, Avila C, Bourne R, Muller F, de Juan A. Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control. Anal Bioanal Chem 2020; 412:2151-2163. [PMID: 31960081 DOI: 10.1007/s00216-020-02404-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 01/08/2020] [Accepted: 01/10/2020] [Indexed: 11/29/2022]
Abstract
Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.
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Affiliation(s)
- Rodrigo R de Oliveira
- Chemometrics Group, Department of Analytical Chemistry, Universitat de Barcelona, Diagonal 645, 08028, Barcelona, Spain.
| | - Claudio Avila
- School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Richard Bourne
- School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Frans Muller
- School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Anna de Juan
- Chemometrics Group, Department of Analytical Chemistry, Universitat de Barcelona, Diagonal 645, 08028, Barcelona, Spain
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Grassi S, Strani L, Casiraghi E, Alamprese C. Control and Monitoring of Milk Renneting Using FT-NIR Spectroscopy as a Process Analytical Technology Tool. Foods 2019; 8:foods8090405. [PMID: 31547293 PMCID: PMC6770950 DOI: 10.3390/foods8090405] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 09/07/2019] [Accepted: 09/09/2019] [Indexed: 11/16/2022] Open
Abstract
Failures in milk coagulation during cheese manufacturing can lead to decreased yield, anomalous behaviour of cheese during storage, significant impact on cheese quality and process wastes. This study proposes a Process Analytical Technology approach based on FT-NIR spectroscopy for milk renneting control during cheese manufacturing. Multivariate Curve Resolution optimized by Alternating Least Squares (MCR-ALS) was used for data analysis and development of Multivariate Statistical Process Control (MSPC) charts. Fifteen renneting batches were set up varying temperature (30, 35, 40 °C), milk pH (6.3, 6.5, 6.7), and fat content (0.1, 2.55, 5 g/100 mL). Three failure batches were also considered. The MCR-ALS models well described the coagulation processes (explained variance ≥99.93%; lack of fit <0.63%; standard deviation of the residuals <0.0067). The three identified MCR-ALS profiles described the main renneting phases. Different shapes and timing of concentration profiles were related to changes in temperature, milk pH, and fat content. The innovative implementation of MSPC charts based on T2 and Q statistics allowed the detection of coagulation failures from the initial phases of the process.
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Affiliation(s)
- Silvia Grassi
- Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, via G. Celoria 2, 20133 Milan, Italy.
| | - Lorenzo Strani
- Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, via G. Celoria 2, 20133 Milan, Italy
| | - Ernestina Casiraghi
- Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, via G. Celoria 2, 20133 Milan, Italy
| | - Cristina Alamprese
- Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, via G. Celoria 2, 20133 Milan, Italy
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de Juan A, Tauler R. Data Fusion by Multivariate Curve Resolution. DATA HANDLING IN SCIENCE AND TECHNOLOGY 2019. [DOI: 10.1016/b978-0-444-63984-4.00008-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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