BACKGROUND: Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance.
RESULTS: We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model.
CONCLUSIONS: We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

Data-driven based batch process monitoring is of critical importance in ensuring stable operating processes and consistent product quality. For long-duration batch processes, it is unrealistic to involve expensive data to train a statistical model for monitoring. To model the inherently batch-wise and variable-wise dynamics, nonlinearity, and time-varying characteristics, this paper proposes a local learning-based two-dimensional subspace identification (LL-2D-SID) scheme based on the similarity between the ongoing batch and the previous batches. The similarity is estimated by the extended extrapolative time-warping. Unlike the conventional statistical models using rich batch data, LL-2D-SID through online optimizing mechanism using limited batch data still has good prediction performance. The application of the sintering process in the polytetrafluoroethylene production has demonstrated that the LL-2D-SID based process monitoring scheme can not only accurately track temperature changes but also timely give fault alarms with a lower error alarm rate than the other SID-based process monitoring schemes.

Process monitoring systems, e.g., systems based on photodiodes, could be used in laser-based powder bed fusion (PBF-LB/M) to measure various process parameters and process signatures to eventually allow for a local, detailed analysis of the produced parts. Here, simple statements only concerning the occurrence of defects in parts are sufficient in many cases, especially with respect to industrial application. Therefore, a pragmatic approach to rapidly infer the occurrence of defects and their types based on in situ data obtained by commercially available process monitoring systems is introduced. In this approach, a color distribution in form of a histogram is determined for each produced part using layer-wise screenshots of the visualized data provided by the monitoring software. Assessment of the histograms of AlSi10Mg samples, which were processed with different parameter combinations, revealed characteristics depending on the prevailing defect types. These characteristics enable the prediction of the occurring defect types without the necessity to apply conventional downstream testing methods, and thus, a straightforward separation of parts with good quality from defective components. Since the approach presented uses the data visualization of the monitoring software, it can be used even when direct access to the raw data is not provided by the machine manufacturer.

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.
CONCLUSIONS: 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 continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber-physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.

Laser powder bed fusion (LPBF) is an additive manufacturing technology with high practical value. In order to improve the quality of the fabricated parts, process monitoring has become a crucial solution, offering the potential to ensure manufacturing stability and repeatability. However, a cardinal challenge involves discerning a precise correlation between process characteristics and potential defects. This paper elucidates the integration of an off-axis vision monitoring mechanism via a high-speed camera focused on capturing the single-track melting phenomenon. An innovative image processing method was devised to segment the plume and spatters, while Kalman filter was employed for multi-object tracking of the spatters. The features of both the plume and spatters were extracted, and their relationship with molten states was investigated. Finally, the PSO-XGBoost algorithm was utilized to identify five molten states, achieving an accuracy of 92.16%. The novelty of this approach resides in its unique combination of plume characteristics, spatter features, and computationally efficient machine learning models, which collectively address the challenge of limited field of view prevalent in real production scenarios, thereby enhancing process monitoring efficacy. Relative to existing methodologies, the proposed PSO-XGBoost approach offers heightened accuracy, convenience, and appropriateness for the monitoring of the LPBF process. This work provides an effective and novel approach to monitor the LPBF process and evaluate the part fabrication quality for complex and changeable working conditions.

Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.

UNASSIGNED: Mixed-methods approaches promise a deep understanding of psychotherapeutic processes. This study uses qualitative and quantitative data from daily diary entries and daily self-assessments during inpatient treatment. The aim of the study is to get an insight into the similarities and differences between both types of data and how they represent self-organized pattern transitions in psychotherapy. While a complete correlation of results is not expected, we anticipate observing amplifying and subsidiary patterns from both perspectives.
UNASSIGNED: Daily, five MDD patients wrote diaries and completed self-assessments using the Therapy Process Questionnaire, a questionnaire for monitoring the change dynamics of psychotherapy. The data were collected using the Synergetic Navigation System, an online tool for real-time monitoring. Diary entries of the patients described their experiences in everyday life. The qualitative text analysis was conducted using Mixed Grounded Theory, which provided categories representing the patients\' ongoing experiences of transformation and stagnation. The time series data was analyzed using the dynamic complexity algorithm and the pattern transition detection algorithm. Results from qualitative and quantitative analyses were combined and compared. Following the process of data triangulation, the leading perspective came from the theory of self-organization. In addition to presenting the overall results for all five patients, we delve into two specific case examples in greater detail.
UNASSIGNED: Specific and highly diversified diary entries of 5 patients were classified into the categories of perceived pattern stability, noticing improvement, broadening the perspective, critical instability, and experiencing moments of Kairos. Patients reported problems not only related to their disorder (e.g., lack of energy and hopelessness) but also to phases and steps of change, which could be related to the theory of self-organization (e.g., problem attractors, critical fluctuations, pattern transitions, and Kairos). Qualitative and quantitative analysis provide important supplementary results without being redundant or identical.
UNASSIGNED: Data triangulation allows for a comprehensive and multi-perspective understanding of therapeutic change dynamics. The different topics expressed in the diary entries especially help to follow micro-psychological processes, which are far from being a simple reaction to interventions. The way patients experience themselves being in stability or instability and stagnation or transformation is surprisingly close to the general features of self-organizing processes in complex systems.

Near-infrared spectroscopy has become a common quality assessment tool for fishmeal products during the last two decades. However, to date it has not been used for active online quality monitoring during fishmeal processing. Our aim was to investigate whether NIR spectroscopy, in combination with multivariate chemometrics, could actively predict the changes in the main chemical quality parameters of pelagic fishmeal and oil during processing, with an emphasis on lipid quality changes. Results indicated that partial least square regression (PLSR) models from the NIR data effectively predicted proximate composition changes during processing (with coefficients of determination of an independent test set at RCV2 = 0.9938, RMSECV = 2.41 for water; RCV2 = 0.9773, RMSECV = 3.94 for lipids; and RCV2 = 0.9356, RMSECV = 5.58 for FFDM) and were successful in distinguishing between fatty acids according to their level of saturation (SFA (RCV2=0.9928, RMSECV=0.24), MUFA (RCV2=0.8291, RMSECV=1.49), PUFA (RCV2=0.8588, RMSECV=2.11)). This technique also allowed the prediction of phospholipids (PL RCV2=0.8617, RMSECV=0.11, and DHA (RCV2=0.8785, RMSECV=0.89) and EPA content RCV2=0.8689, RMSECV=0.62) throughout processing. NIR spectroscopy in combination with chemometrics is, thus, a powerful quality assessment tool that can be applied for active online quality monitoring and processing control during fishmeal and oil processing.

Solar dehydration processes, implemented with NIR sensors, were studied. The research plan was divided into phases to achieve specific objectives. Phase 1: laboratory tests on micro plants; phase 2: scale transposition tests on small-sized plants - pilot scale; phase 3: tests on commercial systems already in use by Italian SMEs. The realisation of the scheduled activities started with the design, programming, and positioning of NIR sensor for data collection and configuration optimization. NIR spectra were collected in reflectance mode (900-1700 nm) using the MicroNIR1700™ or the MicroNIR On-site W™ (VIAVI Solutions Italia S.r.L., Monza, Italy) portable spectrometers with the MicroNIR Pro ES 1700 software. Spectra were acquired automatically throughout the process by placing the NIR probe over a sample slice, positioned on the intermediate shelf. The probe was thermally insulated to avoid temperature variations. The spectra were first transformed by converting reflectance to absorbance; then the second derivative Savitzky-Golay filter (second order polynomial fit and 21 points) and multiplicative scatter correction were applied to remove potential scatter effects. Aquagrams were calculated from the spectral data. The experiments were carried out with a micro-drying system (45x45x45 cm), and a pilot scale plant available at CREA.IT (Milan). Then, the transfer of the selected configuration was realised using a commercial plant already active in a farm of Pavia area. Different food matrices were tested (pineapple, apple, melon, eggplant, onion). NIR data processing by multivariate analysis was made to prove the reliability of the aquaphotomics approach in detecting the actual end of the drying process. The evaluation of the potential development of dedicated App, easy to consult, to be made available through integration on virtual platform was finally computed.