UV spectroscopy and least square matching for high throughput discrimination of taxanes in commercial formulations and compounded bags

2018 ◽  
Vol 123 ◽  
pp. 143-152
Author(s):  
E. Jaccoulet ◽  
C. Boughanem ◽  
L. Auduteau ◽  
P. Prognon ◽  
E. Caudron
Keyword(s):  
High Throughput ◽  
Uv Spectroscopy ◽  
Least Square ◽  
Least Square Matching

scholarly journals Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5855
Author(s):  
Mohammad Akbar Faqeerzada ◽  
Santosh Lohumi ◽  
Geonwoo Kim ◽  
Rahul Joshi ◽  
Hoonsoo Lee ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Analytical Techniques ◽  
Error Rates ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Analysis Model ◽  
One Class Classifier ◽  
Shortwave Infrared

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes

2016 ◽  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Randomized Clinical Trials ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Phenotypes ◽  
Genes And Environment ◽  
Polygenic Scores ◽  
Research Designs ◽  
Statistical Approaches

Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence—genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G × E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use.  The contributors first present different statistical approaches or strategies to address G × E and G × G interactions with high-throughput sequenced data, including two-stage procedures to identify G × E and G × G interactions, marker-set approaches to assessing interactions at the gene level, and the use of a partial-least square (PLS) approach. The contributors then turn to specific complex phenotypes, research designs, or combined methods that may advance the study of G × E interactions, considering such topics as randomized clinical trials in obesity research, longitudinal research designs and statistical models, and the development of polygenic scores to investigate G × E interactions. Contributors Fatima Umber Ahmed, Yin-Hsiu Chen, James Y. Dai, Caroline Y. Doyle, Zihuai He, Li Hsu, Shuo Jiao, Erin Loraine Kinnally, Yi-An Ko, Charles Kooperberg, Seunggeun Lee, Arnab Maity, Jeanne M. McCaffery, Bhramar Mukherjee, Sung Kyun Park, Duncan C. Thomas, Alexandre Todorov, Jung-Ying Tzeng, Tao Wang, Michael Windle, Min Zhang

scholarly journals LEAST SQUARE MATCHING 1 TO COMBINE THE TWO IMAGES OF NCU AREA

Geoid ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. 117
Author(s):  
Hepi Hapsari Handayani
Keyword(s):  
Least Square ◽  
Least Square Matching

Geometric Error Compensation of Five-Axis Machining Centers Based on On-Machine Workpiece Measurement

2018 ◽  
Vol 12 (2) ◽  
pp. 230-237 ◽  
Author(s):  
Ryuta Sato ◽  
◽  
Keiichi Shirase
Keyword(s):  
Error Compensation ◽  
Geometric Error ◽  
Least Square ◽  
Geometric Errors ◽  
Rotary Axes ◽  
Cutting Test ◽  
Measurement Results ◽  
Simulation And Experiment ◽  
Five Axis ◽  
Least Square Matching

This study proposes an identification and compensation method for the geometric errors of the rotary axes in five-axis machining centers, based on the on-machine measurement results of the machined workpiece. Geometric errors can be identified from the shape geometry of the workpiece machined by five-axis motions because the influence of the errors appears on the shape geometry. An observation equation can be obtained based on the geometric error model and machined shape. The actual geometric errors can be identified by the least square matching of the measured and simulated machined shapes. In order to confirm the effectiveness of the proposed method, an actual cutting test and a simulation are performed. Based on their results, it is confirmed that the proposed method can successfully identify the geometric errors in the simulation. However, these errors cannot be identified in the experiments because a few of them do not have sufficient influences onto the machined shape. On the other hand, although the geometric errors cannot be correctly identified, it is confirmed that the they can be adequately compensated for based on the identified errors in both the simulation and experiment.

scholarly journals Fast Least Square Matching

2019 ◽  
Vol 7 (1) ◽  
pp. 193-210
Author(s):  
Amin Sedaghat ◽  
Nazila Mohammadi ◽  
◽  
Keyword(s):  
Least Square ◽  
Least Square Matching

scholarly journals HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4515 ◽  
Author(s):  
Erica Liberto ◽  
Davide Bressanello ◽  
Giulia Strocchi ◽  
Chiara Cordero ◽  
Manuela Rosanna Ruosi ◽  
...  
Keyword(s):  
Decision Maker ◽  
High Throughput ◽  
Sensory Quality ◽  
Solid Phase ◽  
Screening Method ◽  
Partial Least Square ◽  
Least Square ◽  
Raw Material ◽  
Test Set ◽  
Sensory Score

The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA); (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated; and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of ±2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

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