Development of a Multivariate Predictive Dissolution Model for Tablets Coated with Cellulose Ester Blends

The focus of the present investigation was to develop a predictive dissolution model for tablets coated with blends of cellulose acetate butyrate (CAB) 171-15 and cellulose acetate phthalate (C-A-P) using the design of experiment and chemometric approaches. Diclofenac sodium was used as a model drug. Coating weight gain (X1, 5, 7.5 and 10%) and CAB 171-15 percentage (X2, 33.3, 50 and 66.7%) in the coating composition relative to C-A-P and were selected as independent variables by full factorial experimental design. The responses monitored were dissolution at 1 (Y1), 8 (Y2), and 24 (Y3) h. Statistically significant (p < 0.05) effects of X1 on Y1 and X2 on Y1, Y2, and Y3 were observed. The models showed a good correlation between actual and predicted values as indicated by the correlation coefficients of 0.964, 0.914, and 0.932 for Y1, Y2, and Y3, respectively. For the chemometric model development, the near infrared spectra of the coated tablets were collected, and partial least square regression (PLSR) was performed. PLSR also showed a good correlation between actual and model predicted values as indicated by correlation coefficients of 0.916, 0.964, and 0.974 for Y1, Y2, and Y3, respectively. Y1, Y2, and Y3 predicted values of the independent sample by both approaches were close to the actual values. In conclusion, it is possible to predict the dissolution of tablets coated with blends of cellulose esters by both approaches.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.467 ◽

2005 ◽

Vol 13 (3) ◽

pp. 147-154 ◽

Cited By ~ 6

Author(s):

Wolfgang Becker ◽

Norbert Eisenreich

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Process Conditions ◽

Transmission Spectra ◽

Mean Square ◽

Score Plot ◽

Least Square Regression

Near infrared spectroscopy was used as an in-line control system for the measurement of polypropylene filled with different amounts of Irganox additives. For this purpose transmission probes were installed in an extruder. The probes can withstand temperatures up to 300°C and pressures up to 60 MPa. Transmission spectra of polypropylene mixed with an Irganox additive were recorded. PCA score plot was carried out revealing the influence of varying conditions for the mixing of the sample preparation. Prediction models were generated with partial least square regression which resulted in a model which estimated Irganox with a coefficient of detremination of 0.984 and a root mean square error of prediction of 0.098%. Furthermore the possibilities for controlling process conditions by measuring transmission at a specific wavelength were shown.

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Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

Forest Science ◽

10.1093/forsci/fxab015 ◽

2021 ◽

Author(s):

Silvana Nisgoski ◽

Thaís A P Gonçalves ◽

Júlia Sonsin-Oliveira ◽

Adriano W Ballarin ◽

Graciela I B Muñiz

Keyword(s):

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

New Technologies ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Eucalyptus Species ◽

Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

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Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

Journal of AOAC International ◽

10.1093/jaoac/78.3.802 ◽

1995 ◽

Vol 78 (3) ◽

pp. 802-806 ◽

Cited By ~ 46

Author(s):

José Louis Rodriguez-Otero ◽

Maria Hermida ◽

Alberto Cepeda

Keyword(s):

Near Infrared ◽

Independent Set ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Sample Treatment ◽

Infrared Reflectance ◽

Near Infrared Reflectance ◽

Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

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Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Water ◽

10.3390/w12010261 ◽

2020 ◽

Vol 12 (1) ◽

pp. 261 ◽

Cited By ~ 2

Author(s):

Maria Marques ◽

Ana Álvarez ◽

Pilar Carral ◽

Iris Esparza ◽

Blanca Sastre ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Near Infrared ◽

Diffuse Reflectance Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Prediction Ability ◽

Greenhouse Gas Inventories ◽

Starting Point

Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

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Rapid Determination of Intact Sardine Fat by NIRS Using Surface Interactance Fibre Probe

International Journal of Food Engineering ◽

10.2202/1556-3758.1248 ◽

2007 ◽

Vol 3 (6) ◽

Cited By ~ 3

Author(s):

Musleh Uddin ◽

Sandor Turza ◽

Emiko Okazaki

Keyword(s):

Near Infrared ◽

Rapid Determination ◽

Scatter Correction ◽

Screening Method ◽

Fat Content ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Infrared Spectrometer ◽

Near Infrared Spectrometer

A near-infrared spectrometer equipped with surface interactance optical fiber probe (400-1100 nm) was used to determine the fat content in intact sardine Sardinops melanostictus which is considered one of the important fish species of world aquaculture as well as human food source. The fat contents were 2.6425.52 % and fish weight ranges were between 45.23g and 133.76g. Partial least square regression was used to develop predictive equations for fat where two models (with and without multiplicative scatter correction known as MSC) showed relatively good performances with regression coefficients higher than 0.9 and errors below 1% on a fresh weight basis. Results showed that NIR interactance was a suitable non-destructive screening method for fat content in intact small pelagic fish like sardine.

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Visible and near infrared reflectance spectroscopy for field-scale assessment of Stagnosols properties

Plant Soil and Environment ◽

10.17221/220/2018-pse ◽

2018 ◽

Vol 64 (No. 6) ◽

pp. 276-282 ◽

Cited By ~ 1

Author(s):

Šestak Ivana ◽

Mesić Milan ◽

Zgorelec Željka ◽

Perčin Aleksandra ◽

Stupnišek Ivan

Keyword(s):

Soil Ph ◽

Near Infrared ◽

Soil Depth ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Infrared Reflectance ◽

Field Scale ◽

Near Infrared Reflectance ◽

Vnir Spectroscopy

Spectral data contain information on soil organic and mineral composition, which can be useful for soil quality monitoring. The objective of research was to evaluate hyperspectral visible and near infrared reflectance (VNIR) spectroscopy for field-scale prediction of soil properties and assessment of factors affecting soil spectra. Two hundred soil samples taken from the experiment field (soil depth: 30 cm; sampling grid: 15 × 15 m) were scanned using portable spectroradiometer (350–1050 nm) to identify spectral differences of soil treated with ten different rates of mineral nitrogen (N) fertilizer (0–300 kg N/ha). Principal component analysis revealed distinction between higher- and lower-N level treatments conditioned by differences in soil pH, texture and soil organic matter (SOM) composition. Partial least square regression resulted in very strong correlation and low root mean square error (RMSE) between predicted and measured values for the calibration (C) and validation (V) dataset, respectively (SOM, %: RC2 = 0.75 and RV2 = 0.74; RMSEC = 0.334 and RMSEV = 0.346; soil pH: RC2 = 0.78 and RV2 = 0.62; RMSEC = 0.448 and RMSEV = 0.591). Results indicated that hyperspectral VNIR spectroscopy is an efficient method for measurement of soil functional attributes within precision farming framework.

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Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545813500600 ◽

2014 ◽

Vol 07 (04) ◽

pp. 1350060 ◽

Cited By ~ 13

Author(s):

Tao Pan ◽

Minmiao Li ◽

Jiemei Chen ◽

Haiyan Xue

Keyword(s):

Near Infrared ◽

Glycated Hemoglobin ◽

Correlation Coefficients ◽

Partial Least Square ◽

Least Square ◽

Spectral Determination ◽

Spectroscopic Analyses ◽

Biochemical Indicator ◽

New Strategy

A new strategy for quantitative analysis of a major clinical biochemical indicator called glycated hemoglobin (HbA1c) was proposed. The technique was based on the simultaneous near-infrared (NIR) spectral determination of hemoglobin (Hb) and absolute HbA1c content (Hb • HbA1c) in human hemolysate samples. Wavelength selections were accomplished using the improved moving window partial least square (MWPLS) method for stability. Each model was established using an approach based on randomness, similarity, and stability to obtain objective, stable, and practical models. The optimal wavebands obtained using MWPLS were 958 to 1036 nm for Hb and 1492 to 1858 nm for Hb • HbA1c, which were within the NIR overtone region. The validation root mean square error and validation correlation coefficients of prediction (V-SEP, V-R P ) were 3.4 g L-1 and 0.967 for Hb, respectively, whereas the corresponding values for Hb • HbA1c were 0.63 g L-1 and 0.913. The corresponding V-SEP and V-R P were 0.40% and 0.829 for the relative percentage of HbA1c. The experimental results confirm the feasibility for the quantification of HbA1c based on simultaneous NIR spectroscopic analyses of Hb and Hb • HbA1c.

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Detection of malaria in insectary-reared Anopheles gambiae using near-infrared spectroscopy

10.1101/533802 ◽

2019 ◽

Author(s):

Marta F. Maia ◽

Melissa Kapulu ◽

Michelle Muthui ◽

Martin G. Wagah ◽

Heather M. Ferguson ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Anopheles Gambiae ◽

Near Infrared Spectroscopy ◽

Large Scale ◽

Near Infrared ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Human Malaria ◽

The Impact

AbstractLarge-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. This study demonstrates proof-of-concept that near-infrared spectroscopy (NIRS) is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. By using partial least square regression models based on malaria-infected and uninfected Anopheles gambiae mosquitoes, we showed that NIRS can detect oocyst- and sporozoite-stage Plasmodium falciparum infections with 88% and 95% accuracy, respectively. Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

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Prediction of Soil Texture Distributions by using PLSR and Reflectance Spectroscopy

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6558.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7876-7881

Keyword(s):

Statistical Analysis ◽

Soil Texture ◽

Near Infrared ◽

Diffuse Reflectance Spectroscopy ◽

Principal Component ◽

Reflectance Spectroscopy ◽

Soil Samples ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression

The texture of soil i.e. Sand, Silt and Clay are the most important physical properties of soil for agricultural management. In the agricultural practices to increase the productivity of soil, moisture-holding capacity, aeration and to support the agronomic decisions the knowledge of soil texture is an essential task. For this purpose, the present research gives better results and fast acquisition of soil information with the use of Visible and Near Infrared (Vis- NIR) Diffuse Reflectance Spectroscopy. A total of 30 soil samples from two different locations from Aurangabad, Maharashtra, India were collected and analyzed for soil texture. To detect the soil texture the Vis-NIR DRS has shown levels of accurate results compared to the traditional laboratory method with less time, cost and effort. To measure the reflectance of soil the ASD FieldSpec4 Spectroradiometer (350-2500nm) was used. By the observation of captured spectra by using Spectroradiometer it showed that on the basis of different textural classes the soil samples could be spectrally separable. For database collection and pre-processing, we have used RS3 and ViewSpec Pro software respectively. The statistical analysis by using the combination of Principal Component Analysis (PCA) and Partial Least Square Regression method gives accurate results. To determine the texture of soil sample thirteen features were calculated. The main goal of this research was to determine the soil texture by using statistical methods and to test the performance of VNIR-SWIR reflectance spectroscopy by using the ASD FieldSpec4 Spectroradiometer for estimation of the texture of the soil. The results showed that R2 = 0.99 gives maximum accuracy for clay content and R2 = 0.988 for silt content and R2 = 0.989 for sand. The Root Mean Square Values (RMSE) for clay, silt, and sand are 0.02392, 0.02399 and 0.02289 respectively. With the use of reflectance spectroscopy and statistical analysis by using regression models we can determine the soil properties accurately in very less time.

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