scholarly journals A FT-NIR Process Analytical Technology Approach for Milk Renneting Control

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Silvia Grassi ◽  
Lorenzo Strani ◽  
Cristina Alamprese ◽  
Nicolò Pricca ◽  
Ernestina Casiraghi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Process Analytical Technology ◽  
Reference Method ◽  
Principal Component ◽  
Acquisition Time ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Skimmed Milk ◽  
Analytical Technology ◽  
Fundamental Rheology

The study proposes a process analytical technology (PAT) approach for the control of milk coagulation through near infrared spectroscopy (NIRS), computing multivariate statistical process control (MSPC) charts, based on principal component analysis (PCA). Reconstituted skimmed milk and commercial pasteurized skimmed milk were mixed at two different ratios (60:40 and 40:60). Each mix ratio was prepared in six replicates and used for coagulation trials, monitored by fundamental rheology, as a reference method, and NIRS by inserting a probe directly in the coagulation vat and collecting spectra at two different acquisition times, i.e., 60 s or 10 s. Furthermore, three failure coagulation trials were performed, deliberately changing temperature or rennet and CaCl2 concentration. The comparison with fundamental rheology results confirmed the effectiveness of NIRS to monitor milk renneting. The reduced spectral acquisition time (10 s) showed data highly correlated (r > 0.99) to those acquired with longer acquisition time. The developed decision trees, based on PC1 scores and T2 MSPC charts, confirmed the suitability of the proposed approach for the prediction of coagulation times and for the detection of possible failures. In conclusion, the work provides a robust but simple PAT approach to assist cheesemakers in monitoring the coagulation step in real-time.

scholarly journals Fault detection with moving window PCA using NIRS spectra for monitoring the anaerobic digestion process

2020 ◽  
Vol 81 (2) ◽  
pp. 367-382
Author(s):  
L. Awhangbo ◽  
R. Bendoula ◽  
J. M. Roger ◽  
F. Béline
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Organic Load ◽  
Pig Slurry ◽  
Moving Window ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Load Rate ◽  
Continuously Stirred Tank Reactor ◽  
Adaptive Pca

Abstract Principal component analysis (PCA) is a popular method for process monitoring. However, most processes are time-varying, thus older samples are not representative of the current process status. This led to the introduction of adaptive-PCA based monitoring, such as moving window PCA (MWPCA). In this study, near-infrared spectroscopy (NIRS) responses to digester failures were evaluated to develop a spectral data processing tool. Tests were performed with a spectroscopic probe (350–2,500 nm), using a 35 L mesophilic continuously stirred tank reactor. Co-digestion experiments were performed with pig slurry mixed with several co-substrates. Different stresses were induced by abruptly increasing the organic load rate, changing the feedstock or stopping the stirring. Physicochemical parameters as well as NIRS spectra were acquired for lipid, organic and protein overloads experiments. MWPCA was then applied to the collected spectra for a multivariate statistical process control. MWPCA outputs, Hotelling T2 and residuals Q statistics showed that most of the induced dysfunctions can be detected with variations in these statistics according to a defined criterion based on spectroscopic principles and the process. MWPCA appears to be a multivariate statistical method that could help in decision support in industrial biogas plants.

Detecting and Identifying Spectral Anomalies Using Wavelet Processing

1998 ◽  
Vol 52 (10) ◽  
pp. 1348-1352 ◽  
Author(s):  
Chris L. Stork ◽  
David J. Veltkamp ◽  
Bruce R. Kowalski
Keyword(s):  
Wavelet Transform ◽  
Near Infrared ◽  
Principal Component ◽  
Basis Set ◽  
Multivariate Statistical ◽  
General Applicability ◽  
Statistical Process ◽  
Automated Method ◽  
Infrared Measurements ◽  
Wavelength Scale

An automated method integrating wavelet processing and techniques from multivariate statistical process control (MSPC) is presented, providing a means for the simultaneous localization, detection, and identification of disturbances in spectral data. A defining property of the wavelet transform is its ability to map a one-dimensional chemical spectrum into a two-dimensional function of wavelength and scale. Therefore, unlike the traditional MSPC approach where disturbance detection is carried out in the original wavelength domain by using a single principal component analysis (PCA) model, detection employing wavelet transform processing results in the generation of multiple models within the wavelength-scale domain. Provided that the spectral disturbance can be localized within a subregion of the wavelength-scale domain through an advantageous choice of basis set, the method described allows the identification of the underlying disturbance. The utility of the proposed method in localizing, detecting, and identifying spectral disturbances is demonstrated by using real near-infrared measurements, suggesting its general applicability in spectroscopic monitoring of chemical processes.

scholarly journals Near-Infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Steaming Process of Gastrodiae rhizoma with Multiparameters and Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yamin Zuo ◽  
Jing Yang ◽  
Chen Li ◽  
Xuehua Deng ◽  
Shengsheng Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Process Analytical Technology ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Great Promise ◽  
Gastrodiae Rhizoma ◽  
Mean Square Errors ◽  
Analytical Technology ◽  
Hydroxybenzyl Alcohol

Steaming is a vital unit operation in traditional Chinese medicine (TCM), which greatly affects the active ingredients and the pharmacological efficacy of the products. Near-infrared (NIR) spectroscopy has already been widely used as a strong process analytical technology (PAT) tool. In this study, the potential usage of NIR spectroscopy to monitor the steaming process of Gastrodiae rhizoma was explored. About 10 lab scale batches were employed to construct quantitative models to determine four chemical ingredients and moisture change during the steaming process. Gastrodin, p-hydroxybenzyl alcohol, parishin B, and parishin A were modeled by different multivariate calibration models (SMLR and PLS), while the content of the moisture was modeled by principal component regression (PCR). In the optimized models, the root mean square errors of prediction (RMSEP) for gastrodin, p-hydroxybenzyl alcohol, parishin B, parishin A, and moisture were 0.0181, 0.0143, 0.0132, 0.0244, and 2.15, respectively, and correlation coefficients ( R p 2 ) were 0.9591, 0.9307, 0.9309, 0.9277, and 0.9201, respectively. Three other batches’ results revealed that the accuracy of the model was acceptable and that was specific for next drying step. In addition, the results demonstrated the method was reliable in process performance and robustness. This method holds a great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online monitoring and quality control in the TCM industrial steaming process.

scholarly journals Control and Monitoring of Milk Renneting Using FT-NIR Spectroscopy as a Process Analytical Technology Tool

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 405 ◽  
Author(s):  
Silvia Grassi ◽  
Lorenzo Strani ◽  
Ernestina Casiraghi ◽  
Cristina Alamprese
Keyword(s):  
Process Analytical Technology ◽  
Nir Spectroscopy ◽  
Fat Content ◽  
Multivariate Curve Resolution ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Milk Coagulation ◽  
Set Up ◽  
Technology Tool ◽  
Analytical Technology

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.

Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit

2018 ◽  
Vol 66 (8) ◽  
pp. 665-679
Author(s):  
Hassan Enam Al Mawla ◽  
Andreas Kroll
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Time Pressure ◽  
Principal Component ◽  
Component Analysis ◽  
Differential Pressure ◽  
Foam Formation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Proper Actions

Abstract The formation of foam in amine units is an issue that plant operators and field personnel are confronted with on a regular basis. The inability to take proper actions in due time may result in plant downtime and increased emissions. Steep rises in differential pressure indicate foam formation, and are monitored manually in practice. Antifoaming agent is added in order to reduce foaming, but this is usually carried out under time pressure. Hence, plant operating authorities have expressed a strong interest in a data-driven solution capable of providing an early warning against foaming. The classical univariate alarm associated with differential pressure can be ineffective for foaming detection due to high misdetection rates and its lateness of detection. Modern univariate approaches based on pattern recognition techniques may not be suitable either for an early detection, as no universally distinctive features of differential pressure are observed prior to foaming in the present study. In this contribution, the multivariate statistical process monitoring approach based on principal component analysis (PCA) is applied to the early detection of foaming in a continuously operated Shell Claus Off-gas Treating (SCOT) unit of a major refinery in Germany. The results are extended to facilitate fully automated and adaptive modeling based on exponentially weighted recursive principal component analysis (EWRPCA).

Potential of Near-Infrared Chemical Imaging as Process Analytical Technology Tool for Continuous Freeze-Drying

2018 ◽  
Vol 90 (7) ◽  
pp. 4354-4362 ◽  
Author(s):  
Davinia Brouckaert ◽  
Laurens De Meyer ◽  
Brecht Vanbillemont ◽  
Pieter-Jan Van Bockstal ◽  
Joris Lammens ◽  
...  
Keyword(s):  
Freeze Drying ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Chemical Imaging ◽  
Technology Tool ◽  
Analytical Technology
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