Focusing near Infrared Spectroscopy on the Business Objectives of Modern Pharmaceutical Production

1998 ◽  
Vol 6 (A) ◽  
pp. A13-A19 ◽  
Author(s):  
T.G. Axon ◽  
R. Brown ◽  
S.V. Hammond ◽  
S.J. Maris ◽  
F. Ting
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Real Life ◽  
Nir Spectroscopy ◽  
Raw Material ◽  
Develop Model ◽  
Data Sets ◽  
Total Quality ◽  
Model Free ◽  
Pharmaceutical Production

The early use of near infrared (NIR) spectroscopy in the pharmaceutical industry was for raw material identification, later moving on to some conventional “calibrations” for various ingredients in a variety of sample types. The approach throughout this development process has always been “conventional” with one measurement by NIR directly replacing some other slower method, be it Mid-IR identification, or determinations by Karl Fischer, high performance liquid chromatography (HPLC)etc. A significant change in approach was demonstrated by Plugge and Van der Vlies1 in 1993, where a qualitative system was used to provide “quantitative like” answers for potency of a drug substance. Following on from that key paper, there has been a realisation that the qualitative analysis ability of NIR, has the potential to be a powerful tool for process investigation, control and validation. The final step has been to develop “model free” approaches, that consider individual data sets as unique systems, and present the opportunity for NIR to escape the shackles of “calibration” in one form or another. The use of qualitative, or model free, approaches to NIR spectroscopy provides an effective tool for satisfying many of the demands of modern pharmaceutical production. “Straight through production,” “right first time,” “short cycle time” and “total quality management” philosophies can be realised. Eventually the prospect of parametric release may be materialised with a strong contribution from NIR spectroscopy. This paper will illustrate the above points with some real life examles.

scholarly journals Discrimination of Salvia miltiorrhiza Bunge from Different Geographical Locations Employing High-Performance Liquid Chromatography, Near-Infrared Fingerprinting Combined with Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiao Wang ◽  
Yichun Sun ◽  
Zhan Li ◽  
Wei Li ◽  
Yuanyuan Pang ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Discriminant Analysis ◽  
High Performance ◽  
Near Infrared ◽  
Salvia Miltiorrhiza ◽  
Nir Spectroscopy ◽  
Gradient Elution ◽  
Array Detector ◽  
Geographical Locations

To evaluate the quality of Salvia miltiorrhiza Bunge, high-performance liquid chromatography-diode array detector (HPLC/UV-PAD), near infrared (NIR) spectroscopy, and chemometrics were used to discriminate nine components of samples from four different geographical locations. HPLC was performed with a C18 (5 μm, 4.6 mm × 250 mm) column and 0.1% formic acid aqueous solution-acetonitrile with a gradient elution system. Orthogonal partial least squares discriminant analysis was used to identify the amounts of salvianolic acid B. NIR was used to distinguish rapidly S. miltiorrhiza Bunge samples from different geographical locations. In this assay, discriminant analysis was performed, and the accuracy was found to be 100%. The combination of these two methods can be used to quickly and accurately identify S. miltiorrhiza Bunge from different geographical locations.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

Estimation of Eucalyptus delegatensis wood properties by near infrared spectroscopy

2001 ◽  
Vol 31 (10) ◽  
pp. 1671-1675 ◽  
Author(s):  
L R Schimleck ◽  
R Evans ◽  
J Ilic
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Raw Material ◽  
Modulus Of Rupture ◽  
Single Type ◽  
Wide Range ◽  
Eucalyptus Delegatensis

The use of calibrated near infrared (NIR) spectroscopy for the prediction of a range solid wood properties is described. The methods developed are applicable to large-scale nondestructive forest resource assessment and to tree breeding and silvicultural programs. A series of Eucalyptus delegatensis R.T. Baker (alpine ash) samples were characterized in terms of density, longitudinal modulus of elasticity (EL), microfibril angle (MFA), and modulus of rupture (MOR). NIR spectra were obtained from the radial–longitudinal face of each sample and used to generate calibrations for the measured physical properties. The relationships were good in all cases, with coefficients of determination ranging from 0.77 for MOR through 0.90 for EL to 0.93 for stick density. In view of the rapidly expanding range of applications for this technique, it is concluded that appropriately calibrated NIR spectroscopy could form the basis of a "universal" testing instrument capable of predicting a wide range of product properties from a single type of spectrum obtained from the product or from the raw material.

Evaluation of Soya Bean Quality for Tofu Processing by near Infrared Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A325-A328
Author(s):  
T.L. Hong ◽  
Samson C.S. Tsou ◽  
S.-J. Tsai
Keyword(s):  
Water Absorption ◽  
Absorption Rate ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soya Bean ◽  
Control Measures ◽  
Raw Material ◽  
Prolonged Storage ◽  
Tannin Content ◽  
Water Absorption Rate

Soya bean, as the raw material for tofu processing, is required to be of high quality. The variety characteristics, storage conditions and harvesting seasons of soya bean are the major contributors to soya bean quality. This study attempted to use near infrared (NIR) spectroscopy to evaluate the processing quality of soya bean. Evaluation models using NIR spectroscopy were developed for the analyses of tannin content, degrees of lipid oxidation, detection of harvest seasons and measurement of water absorption rate. Simulation experiments demonstrated that these models were not only able to analyse major compositions of soya bean, but also to sort out soya bean samples and their suitability for tofu making regardless of various defects, such as high tannin content, low water absorption rate, prolonged storage and unfavourable harvest seasons. Statistic analysis suggested that these models could be used as mass-screening techniques for breeding programmes and quality control measures in tofu-processing factories.

scholarly journals Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals

2016 ◽  
Vol 71 (3) ◽  
pp. 520-532 ◽  
Author(s):  
José A. Adame-Siles ◽  
Tom Fearn ◽  
José E. Guerrero-Ginel ◽  
Ana Garrido-Varo ◽  
Francisco Maroto-Molina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Animal Health ◽  
Real Life ◽  
Nir Spectroscopy ◽  
Geostatistical Analysis ◽  
Structure Characterization ◽  
Sampling Point ◽  
Fiber Optic Probe ◽  
Effective Manner

Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

scholarly journals Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 825 ◽  
Author(s):  
Fadi Al Machot ◽  
Mohammed R. Elkobaisi ◽  
Kyandoghere Kyamakya
Keyword(s):  
Activity Recognition ◽  
High Performance ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sensor Data ◽  
Sensor Technology ◽  
Training Dataset ◽  
Data Sets ◽  
Data Set

Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life settings should cover a growing number of activities in various domains, whereby a significant part of instances will not be present in the training data set. However, to cover all possible activities in advance is a complex and expensive task. Concretely, we need a method that can extend the learning model to detect unseen activities without prior knowledge regarding sensor readings about those previously unseen activities. In this paper, we introduce an approach to leverage sensor data in discovering new unseen activities which were not present in the training set. We show that sensor readings can lead to promising results for zero-shot learning, whereby the necessary knowledge can be transferred from seen to unseen activities by using semantic similarity. The evaluation conducted on two data sets extracted from the well-known CASAS datasets show that the proposed zero-shot learning approach achieves a high performance in recognizing unseen (i.e., not present in the training dataset) new activities.

scholarly journals Evaluation of nutrient content in red kidney beans, amaranth leaves, sweet potato roots and carrots cultivated in Rwanda

2021 ◽  
Vol 21 (04) ◽  
pp. 17801-17814
Author(s):  
Marie-Rose Kambabazi ◽  
◽  
MW Okoth ◽  
S Ngala ◽  
L Njue ◽  
...  
Keyword(s):  
Vitamin A ◽  
Vitamin C ◽  
Sweet Potato ◽  
High Performance ◽  
Near Infrared ◽  
Dietary Diversity ◽  
Nutrient Content ◽  
Raw Material ◽  
Kidney Beans ◽  
Red Kidney Beans

No data exist on the nutrient composition of some important Rwandan staples. The aim of this study was to evaluate the nutrient content of red kidney beans, sweet potato roots, amaranth leaves and carrot roots. About 6 kg of each raw material were cleaned and conditioned prior to mechanical drying, ground and sieved [60-mesh] into flour and then subjected to quantitative analysis for proximate content,energy, calcium (Ca), iron (Fe), zinc (Zn), vitamin A and vitamin C. Proximate composition determination was done using Near Infrared Spectroscopy (NIRS), carbohydrates were determined by difference, energy was calculated, mineral analysis was done by Atomic Absorption Spectroscopy (AAS) and vitamin analysis was performed by High Performance Liquid Chromatography (HPLC) methods. The results showed that red kidney beans, sweet potato roots, amaranth leaves and carrots contain 21.48, 6.66, 29.46 and 13.8% of protein; 2.58, 1.68, 7.89 and 2.08% of fat; 60.86, 79.13, 19.29 and 57.38% of carbohydrate; 2.33, 2.68, 8.98 and 9.63% of fiber; 8.82, 8.74, 10.08 and 8.88% of moisture content; 3.94, 1.11, 24.30 and 5.16% of ash; 357.2, 363.7, 284.0, 322.9 kcal/100g of energy; and 146.4, 182.7, 26,290 and 1,247mg/kg of calcium,respectively. Red kidney beans, amaranth leaves and carrots contained8.54, 30.48, and 15.55 mg/kg of zinc; and 21.36, 219.1and 8.81 mg/kg of iron,respectively. Zinc and iron were,however,not detected in sweet potato samples analysed. Red kidney beans, sweet potato roots, amaranth leaves and carrot contained 768.0, 10,880, 399.4, and 6,413 IU/100g of vitamin A; and 2.67, 30.99, 330.3 and 6.76 mg/100g of vitamin C,respectively. In conclusion, the staples analysed contained appreciable amounts of nutrients and could be used to overcome malnutrition and allow dietary diversity. It could be recommended to prepare a Rwandanfood composition database in order to improve awareness on local grown crops’ quality.

Classification of Soil Texture Classes by Using Soil Visual near Infrared Spectroscopy and Factorial Discriminant Analysis Techniques

2005 ◽  
Vol 13 (4) ◽  
pp. 231-240 ◽  
Author(s):  
A.M. Mouazen ◽  
R. Karoui ◽  
J. De Baerdemaeker ◽  
H. Ramon
Keyword(s):  
Soil Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Data Sets ◽  
Validation Data ◽  
Light Reflectance ◽  
Validation Set ◽  
Soil Groups

Texture is one of the main properties affecting the accuracy of visible (vis) and near infrared (NIR) spectroscopy during on-the-go measurement of soil properties. Classification of soil spectra into predefined texture classes is expected to increase the accuracy of measurement of other soil properties using separate groups of calibration models, each developed for one texture class. A mobile, fibre-type, vis-NIR spectrophotometer (Zeiss Corona 1.7 vis-NIR fibre), with a light reflectance measurement range of 306.5–1710.9 nm was used to measure the light reflectance from fresh soil samples collected from many fields in Belgium and northern France. A total of 365 soil samples were classified into four different texture classes, namely, coarse sandy, fine sandy, loamy and clayey soils. The factorial discriminant analysis (FDA) was applied on the first five principal components obtained from the principal component analysis performed on the vis-NIR spectra in order to classify soils into the four assigned groups. Correct classification (CC) of 85.7% and 81.8% was observed for the calibration and validation data sets, respectively. However, validation of the vis-NIR-FDA technique on the validation set showed poor discrimination between the coarse sandy and fine sandy soil groups, with a great deal of overlapping. Therefore, the soil groups were reduced to three groups by combining the coarse sandy and fine sandy soil groups into one group and FDA was applied again. A better classification was obtained with CC of 89.9 and 85.1% for the calibration and validation data sets, respectively. However, the CC for the sand group in the validation set was rather small (46.7%), which was attributed to the small sample number and poor correlation between sand fraction and vis-NIR spectroscopy. It was concluded that vis-NIR-FDA is an efficient technique to classify soil into three main groups of sandy (light soils), loamy (medium soils) and clayey (heavy soils). Additional samples from the sandy and clayey groups should be included to improve the accuracy of the vis-NIR-FDA classification models to be used for an on-the-go vis-NIR measurement system of soil properties.

scholarly journals Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 785
Author(s):  
Yisen Liu ◽  
Songbin Zhou ◽  
Wei Han ◽  
Chang Li ◽  
Weixin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Infant Formula ◽  
Ensemble Learning ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Data Sets ◽  
Promising Tool ◽  
Random Initialization ◽  
Non Destructive

Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R2) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively.

Damp Heat Aging Behavior of a Polyamide-Based Backsheet for Photovoltaic Modules

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Klaus J. Geretschläger ◽  
Gernot M. Wallner ◽  
Ingrid Hintersteiner ◽  
Wolfgang Buchberger
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Heat Exposure ◽  
Accelerated Aging ◽  
Nir Spectroscopy ◽  
Attenuated Total Reflection ◽  
Gravimetric Analysis ◽  
Scanning Calorimetry ◽  
Premature Failure ◽  
Mechanical Methods

This paper describes and evaluates accelerated aging of a titanium dioxide (TiO2) filled polyamide (PA) based backsheet film for photovoltaic (PV) modules. Damp heat exposure (85%RH, 85 °C) was carried out up to 2000 hrs. The backsheet was characterized using microscopic, spectroscopic, thermoanalytic, chromatographic, and mechanical methods. While Raman microscopy, infrared spectroscopy in attenuated total reflection mode (IR-ATR), scanning calorimetry (DSC), and thermal gravimetric analysis did not reveal aging-induced changes, significant yellowing was detected by ultraviolet visible near infrared (UV/VIS/NIR) spectroscopy. Depending on the stabilizer type (UV-absorbers, hindered amine light stabilizers (HALSs), and antioxidants), rather different consumption rates were ascertained by high-performance liquid chromatography (HPLC) and gas chromatography (GC). Although the ultimate mechanical properties decreased significantly, no full embrittlement was obtained after damp heat exposure of up to 2000 hrs. The observed physical and chemical aging mechanisms were classified as within the induction period without premature failure.

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