Abstract An increasingly large dataset of pharmaceutics disciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al. . in AAPS PharmSciTech 23, 2022 ) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robust data on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset ( http://www.models.life.ku.dk/Tablets ) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. Highlights • We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies • A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets • Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance • The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation • Model extension from lab-scale to pilot-scale successfully demonstrated Graphical Abstract

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Machine Learning–Enabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data

Quercus suber L. bark (cork) is a sustainable material due to its ability to regenerate. The aim of this work was to explore cork powders, by-products of the cork industry, as sustainable sources of value-added compounds. Two types of cork powder were studied: coarse (P0) and fine (P1). A broad physicochemical characterization was carried out, regarding particle size, color, moisture content, hygroscopicity, pH, heavy metal content, NIR spectra, and volatile compounds. DPPH scavenging activity and total phenolic content were also evaluated for an ethanolic P1 extract. For both powders, Hg, As, Cd and Pb contents were below the acceptable limits and volatile compounds commonly used as fragrances were found in their composition. P1 had a smaller and more homogenous particle size, lighter brownish color, lower pH value, and lower moisture content than P0, and therefore appears to be more suitable for industrial application. P1 ethanolic extract also showed a high scavenging activity and a content of phenolic compounds of 50.46 ± 0.63 mg (as gallic acid equivalents) per g of extract. In conclusion, P1 cork powder seems to be a promising source of upcycled ingredients, such as fragrances and antioxidants, for the pharmaceutical, nutraceutical, and cosmetic industries.

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Quercus suber Bark as a Sustainable Source of Value-Added Compounds: Experimental Studies with Cork By-Products

Quality evaluation of white sugar crystals using the friability test and their non-destructive prediction using near-infrared spectroscopy

Quality Evaluation of White Sugar Crystals Using the Friability Test and Their Non-Destructive Prediction Using Near-Infrared Spectroscopy

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model’s performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Graphical Abstract Impact of various data learning approaches on NIR spectral data

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Manohar Rao

Papers

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts