Prediction of Ice Fraction and Fat Content in Super-Chilled Salmon by Non-Contact Interactance near Infrared Imaging

2009 ◽  
Vol 17 (2) ◽  
pp. 77-87 ◽  
Author(s):  
Silje Ottestad ◽  
Martin Høy ◽  
Astrid Stevik ◽  
Jens Petter Wold
Keyword(s):  
Shelf Life ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Fat Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Multivariate Curve Resolution ◽  
Prediction Ability ◽  
Ice Fraction

It has been shown that super-chilling can increase the shelf life of fish and meat products without the reduction in quality encountered with normal freezing. The shelf life and quality achieved depend on the fraction of water frozen and how it is preserved in the product. In this paper, we report on the possibility of using non-contact near infrared (NIR) interactance spectroscopy in combination with multi-spectral imaging as a rapid, non-destructive way to predict the average ice fraction as well as the spatial distribution of ice in super-chilled salmon fillets. For average ice fraction calibrations, samples in the range of 0% to 30% ice were investigated. Partial least square regression (PLSR) models with high explained variance ( R2≈0.98) and root mean square error of cross-validation between 1.6% and 2.4% were obtained. The time of NIR measurement after super-chilling (0 h, 1/2 h, 2 h and 24 h) did not seem to affect the prediction ability significantly. The spatial ice distribution, as predicted by PLSR modelling, was affected considerably by the fat distribution. This was avoided by the use of multivariate curve resolution at pixel level and calibration against the amount of ice divided by the total amount of water in both liquid and solid state. Excellent images of ice distribution were achieved and allowed observation of changes during storage and thawing. The ability to predict fat content in super-chilled salmon fillets was also investigated. Fat content predictions were affected by ice fraction, but were observed to be independent of time of measurement after freezing.

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

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 261 ◽  
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.

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

2007 ◽  
Vol 3 (6) ◽  
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 2.64–25.52 % 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.

A Consensus Partial Least Squares Regression for Analysis of Near-Infrared Spectroscopy

2013 ◽  
Vol 765-767 ◽  
pp. 528-531
Author(s):  
Dan Peng ◽  
Qing Chen Nie
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Successful Model ◽  
Consensus Strategy

To improve the prediction performance of partial least square regression algorithm (PLS), the consensus strategy was applied to develop the multivariate regression model using near-infrared (NIR) spectra and named as C-PLS. Coupled with the consensus strategy, this algorithm can take the advantage of reducing dependence on single model to obtain prediction precision and stability by randomly changing the calibration set. Through an optimization of the parameters involved in the model including criterion threshold and number of sub-models, a successful model was achieved by effectively combining many sub-models with different accuracy and diversity together. To validate the C-PLS algorithm, it was applied to measure the original extract concentration of beer using NIR spectra. The experimental results showed that the prediction ability and robustness of model obtained in subsequent partial least squares calibration using consensus strategy was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 45.2%, indicating that it is an efficient tool for NIR spectra regression.

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

Foods ◽  
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.

Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

2005 ◽  
Vol 13 (3) ◽  
pp. 147-154 ◽  
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.

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

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.

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

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
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.

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

2018 ◽  
Vol 64 (No. 6) ◽  
pp. 276-282 ◽  
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, %: R<sub>C</sub><sup>2</sup> = 0.75 and R<sub>V</sub><sup>2</sup> = 0.74; RMSE<sub>C</sub> = 0.334 and RMSE<sub>V</sub> = 0.346; soil pH: R<sub>C</sub><sup>2</sup> = 0.78 and R<sub>V</sub><sup>2</sup> = 0.62; RMSE<sub>C</sub> = 0.448 and RMSE<sub>V</sub> = 0.591). Results indicated that hyperspectral VNIR spectroscopy is an efficient method for measurement of soil functional attributes within precision farming framework.  

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

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.

scholarly journals Prediction of Soil Texture Distributions by using PLSR and Reflectance Spectroscopy

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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