Evaluation of soluble solids of curry soup containing coconut milk by near infrared spectroscopy

2017 ◽  
Vol 25 (3) ◽  
pp. 203-210 ◽  
Author(s):  
Panmanas Sirisomboon ◽  
Jutarat Nawayon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Total Sample ◽  
Coconut Milk ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Robust Model ◽  
Processing Line

The aim of this research was to do a feasibility study of near infrared spectroscopy to evaluate soluble solids of curry soup containing coconut milk. The soup samples were collected from mixing tanks, water adjusting tanks, an ultra-high temperature process line and laminated cartons. There were also soluble solids adjusted samples by adding or reducing coconut sugar where the curry was made from the same recipe as in the processing line but increasing 30, 60 and 90% coconut sugar and reducing 30, 60 and 90% coconut sugar from normal. There were 119 samples in total. Sample was scanned with an FT-NIR spectrometer. A prediction model for soluble solids was established using near infrared spectral data in conjunction with partial least squares regression. When validated using a set of test samples, the model developed using spectra pretreated by min-max normalization in the range 9403.8–6094.3 cm−1, provided a coefficient of determination (r2), root mean square error of prediction, bias and ratio of performance to interquartile of 0.92, 1.0°Brix, 0.1°Brix and 2.4, respectively. It showed the potential of using near infrared spectroscopy to evaluate soluble solids in curry soup. With further development using more natural samples, a more robust model could be achieved to evaluate soluble solids in curry soup in a processing factory.

Rapid evaluation of fat content in curry soup containing coconut milk by using near infrared spectroscopy

2017 ◽  
Vol 26 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Ekkapong Cheevitsopon ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fat Content ◽  
Coconut Milk ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Test Set ◽  
Processing Line

The feasibility of a near infrared spectroscopy to evaluate the fat content in instant curry soup containing coconut milk including green curry, red curry, massaman curry and panang curry was investigated. The soup samples were collected from a processing line and as the finished product. There were also fat content-adjusted samples where the curry was made from the same recipe as in the processing line but increasing by 30, 60 and 90% coconut milk and reducing by 30, 60 and 90% coconut milk from normal. A Fourier transform near infrared spectrometer was used to collect scans. A partial least squares regression model for fat content was established using near infrared spectral data in conjunction with reference data, which was validated using a leave-one-out cross-validation and test set validation. The test set validation, using a set of unknown samples, showed better prediction performance. The best model developed using vector normalization spectral pre-treatment on 9404–7498 and 6102–5446 cm−1 provided coefficient of determination, root mean square error of prediction, bias and ratio of performance to interquartile values of 0.90, 0.9%, −0.1% and 1.2, respectively, for the validation samples. However, the model developed using samples without fat content adjusted samples gave a slightly lower coefficient of determination (0.89), but provided a lower root mean square error of prediction (0.5%) and acceptable ratio of standard error of validation to the standard deviation (3.2). In addition, the vibration bands of CH2 which was in the long chain fatty acid moiety highly influenced the prediction of fat content in the curry soup. The near infrared spectroscopy protocol developed for the determination of fat could be applied in the instant curry soup production line.

scholarly journals OTIMIZAÇÃO DE CALIBRAÇÕES BASEADAS EM ESPECTROSCOPIA NO INFRAVERMELHO PRÓXIMO PARA ESTIMATIVA DE PROPRIEDADES DA MADEIRA DE Eucalyptus

FLORESTA ◽  
2010 ◽  
Vol 40 (3) ◽  
Author(s):  
Paulo Ricardo Gherardi Hein ◽  
José Tarcísio Lima ◽  
Gilles Chaix Gilles Chaix
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Laboratory Data ◽  
Wood Properties ◽  
Coefficient Of Determination ◽  
Mathematical Treatment ◽  
Least Squares Regression ◽  
Eucalyptus Wood ◽  
Prediction Density

A espectroscopia no infravermelho próximo (NIRS) é uma técnica não-destrutiva, rápida e utilizada para avaliação, caracterização e classificação de materiais, sobretudo de origem biológica. A obtenção de informações contida nos espectros NIR é complexa e requer a utilização de métodos quimiométricos. Assim, por meio de regressão multivariada, os espectros de absorbância podem ser associados às propriedades da madeira, tornando possível a sua predição em amostras desconhecidas. Existem algumas ferramentas quimiométricas que melhoram o ajuste dos modelos preditivos. Assim, o objetivo deste trabalho foi simular regressões dos mínimos quadrados parciais baseados nas informações espectrais e de laboratório e estudar a influência da aplicação de tratamentos matemáticos, do descarte de amostras anômalas e da seleção de comprimentos de onda no ajuste dos modelos para estimativa da densidade básica e do módulo de elasticidade em ensaio de compressão paralela às fibras da madeira de Eucalyptus. A aplicação da primeira e segunda derivada nos espectros, o descarte de amostras anômalas e a seleção de algumas das variáveis espectrais melhorou significativamente o ajuste do modelo, reduzindo o erro padrão e aumentando o coeficiente de determinação e a relação de desempenho do desvio.Palavras-chave:  Espectroscopia no infravermelho próximo; predição; densidade básica; MOE; madeira; Eucalyptus. AbstractOptimization of calibrations based on near infrared spectroscopy for estimation of Eucalyptus wood properties. Near infrared spectroscopy (NIRS) is a non-destructive technique used for rapid evaluation, characterization and classification of biological materials. The extraction of the information contained in the NIR spectrum is complex and requires the use of chemo metric methods. Thus, by means of multivariate regression, the absorbance spectra are correlated to wood properties, making possible the prediction in unknown samples. There are some chemo metric tools that can improve the adjustment of the predictive models. The aim of this work was to simulate partial least squares regression based on NIR spectra and laboratory data and to study the influence of the application of mathematical treatment, the removal of outliers and the wavelengths selection in the adjustment of models to estimate the density and modulus of elasticity in Eucalyptus wood. The use of the first and second derivative spectra, the disposal of outliers, and the variables selection improved significantly the model fit, reducing the standard error and increasing the coefficient of determination and the ratio of performance to deviation.Keywords: Near infrared; spectroscopy; prediction; density; MOE; wood; Eucalyptus.

scholarly journals Prediction of water content and soluble solids content of ‘manalagi’ apples using near infrared spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012062
Author(s):  
K Kusumiyati ◽  
Y Hadiwijaya ◽  
D Suhandy ◽  
A A Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Water Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quality Attributes ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Soluble Solids Content ◽  
Orthogonal Signal Correction ◽  
Solids Content

Abstract The purpose of the research was to predict quality attributes of ‘manalagi’ apples using near infrared spectroscopy (NIRS). The desired quality attributes were water content and soluble solids content. Spectra data collection was performed at wavelength of 702 to 1065 nm using a Nirvana AG410 spectrometer. The original spectra were enhanced using orthogonal signal correction (OSC). The regression approaches used in the study were partial least squares regression (PLSR) and principal component regression (PCR). The results showed that water content prediction acquired coefficient of determination in calibration set (R2cal) of 0.81, coefficient of determination in prediction set (R2pred) of 0.61, root mean squares error of calibration set (RMSEC) of 0.009, root mean squares of prediction set (RMSEP) of 0.020, and ratio performance to deviation (RPD) of 1.62, while soluble solids content prediction displayed R2cal, R2pred, RMSEC, RMSEP, and RPD of 0.79, 0.85, 0.474, 0.420, and 2.69, respectively. These findings indicated that near infrared spectroscopy could be used as an alternative technique to predict water content and soluble solids content of ‘manalagi’ apples.

scholarly journals Time-of-flight Near-infrared Spectroscopy for Nondestructive Measurement of Internal Quality in Grapefruit

2013 ◽  
Vol 138 (3) ◽  
pp. 225-228 ◽  
Author(s):  
Yohei Kurata ◽  
Tomoe Tsuchida ◽  
Satoru Tsuchikawa
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Time Of Flight ◽  
Soluble Solids ◽  
Internal Quality ◽  
Citrus Paradisi ◽  
Least Squares Regression ◽  
Time Resolved

We proposed a technique combining time-of-flight (TOF) and near-infrared spectroscopy (NIRS), termed TOF-NIRS, capable of measuring the time-resolved profiles of near-infrared (NIR) light with nanosecond resolution. Analysis of the variation in time-resolved profiles was used to estimate soluble solids concentration (SSC) and acidity in grapefruit (Citrus paradisi), and the prediction accuracy was compared with the conventional NIR measurement device. In data processing, the cross-correlation function, which evaluated the similarity between the reference and transmitted beams, was introduced as an explanatory variable for partial least squares regression. TOF-NIRS predicted both SSC and acidity in grapefruit with higher precision than the conventional NIR measurement with respective r values of 0.72 and 0.85. Specifically, the superiority of TOF-NIRS was attributed to measurement time and prediction accuracy in determining acidity.

scholarly journals Estimate of the density of Eucalyptus grandis W. Hill ex Maiden using near infrared spectroscopy

CERNE ◽  
2013 ◽  
Vol 19 (4) ◽  
pp. 647-652 ◽  
Author(s):  
Silviana Rosso ◽  
Graciela Ines Bolzon de Muniz ◽  
Jorge Luis Monteiro de Matos ◽  
Clóvis Roberto Haselein ◽  
Paulo Ricardo Gherardi Hein ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Basic Density ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Old Trees

This study aimed to analyze use of near infrared spectroscopy (NIRS) to estimate wood density of Eucalyptus grandis. For that, 66 27-year-old trees were logged and central planks were removed from each log. Test pieces 2.5 x 2.5 x 5.0 cm in size were removed from the base of each plank, in the pith-bark direction, and subjected to determination of bulk and basic density at 12% moisture (dry basis), followed by spectral readings in the radial, tangential and transverse directions using a Bruker Tensor 37 infrared spectrophotometer. The calibration to estimate wood density was developed based on the matrix of spectra obtained from the radial face, containing 216 samples. The partial least squares regression to estimate bulk wood density of Eucalyptus grandis provided a coefficient of determination of validation of 0.74 and a ratio performance deviation of 2.29. Statistics relating to the predictive models had adequate magnitudes for estimating wood density from unknown samples, indicating that the above technique has potential for use in replacement of conventional testing.

Potential of near infrared spectroscopy as a rapid method to detect aflatoxins in brown rice

2019 ◽  
Vol 27 (3) ◽  
pp. 232-240 ◽  
Author(s):  
C Dachoupakan Sirisomboon ◽  
P Wongthip ◽  
P Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Rapid Method ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brown Rice ◽  
Predictive Performance ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Health Food ◽  
Practical Applications

Brown rice is a main popular health food with high nutritional value and health benefits. As a result of poor post-harvest drying and inappropriate storage conditions, rice grains are often damaged through fungal spoilage as well as mycotoxin production. The objective of this research was to evaluate the possibility of using the near infrared spectroscopy, with a wavenumber range between 12500 and 4000 cm−1 (800–2500 nm), as a rapid method for detection of aflatoxins in brown rice. Firstly, storage trials were carried out to generate representative of samples contaminated and non-contaminated with aflatoxins. These data were used to create a partial least squares regression model using 120 brown rice samples with the required near infrared spectral data and aflatoxin concentration levels that were determined using a standard enzyme-linked immunosorbent assays method. The accuracy of developed models was externally validated using the test set. The statistical model developed from the treated spectra (vector normalization; SNV) provided the best accuracy in prediction with a coefficient of determination of prediction ( r2) of 0.95, a root mean square error of prediction of 415.00 µg kg−1 and a bias −54.00 µg kg−1. The model developed showed good predictive performance which suggests that it could have practical applications as a rapid method to detect aflatoxins in brown rice.

scholarly journals Near Infrared Spectroscopy as a Green Technology for the Quality Prediction of Intact Olives

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1042
Author(s):  
Silvia Grassi ◽  
Olusola Samuel Jolayemi ◽  
Valentina Giovenzana ◽  
Alessio Tugnolo ◽  
Giacomo Squeo ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Olive Oil ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Green Technology ◽  
Soluble Solids ◽  
Integrating Sphere ◽  
Total Phenolic ◽  
Handheld Device ◽  
Quality Design

Poorly emphasized aspects for a sustainable olive oil system are chemical analysis replacement and quality design of the final product. In this context, near infrared spectroscopy (NIRS) can play a pivotal role. Thus, this study aims at comparing performances of different NIRS systems for the prediction of moisture, oil content, soluble solids, total phenolic content, and antioxidant activity of intact olive drupes. The results obtained by a Fourier transform (FT)-NIR spectrometer, equipped with both an integrating sphere and a fiber optic probe, and a Vis/NIR handheld device are discussed. Almost all the partial least squares regression models were encouraging in predicting the quality parameters (0.64 < R2pred < 0.84), with small and comparable biases (p > 0.05). The pair-wise comparison between the standard deviations demonstrated that the FT-NIR models were always similar except for moisture (p < 0.05), whereas a slightly lower performance of the Vis/NIR models was assessed. Summarizing, while on-line or in-line applications of the FT-NIR optical probe should be promoted in oil mills in order to quickly classify the drupes for a better quality design of the olive oil, the portable and cheaper Vis/NIR device could be useful for preliminary quality evaluation of olive drupes directly in the field.

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 301-310 ◽  
Author(s):  
Jetsada Posom ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Development ◽  
Volatile Matter ◽  
Ash Content ◽  
Least Squares Regression ◽  
Fixed Carbon ◽  
Bomb Calorimetry ◽  
Heating Value

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

scholarly journals Rapid Diagnosis of HIV-1 virus by Near Infrared Spectroscopy: based on Partial least squares regression

2021 ◽  
Vol 271 ◽  
pp. 03067
Author(s):  
Xiaohong He ◽  
Zhihong Song ◽  
Haifei Shang ◽  
Silang Yang ◽  
Lujing Wu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
False Negative ◽  
Cost Effective ◽  
Rapid Diagnosis ◽  
Enzyme Linked Immunosorbent Assay ◽  
Least Squares Regression ◽  
Early Screening ◽  
Hiv 1

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV.

Sign in / Sign up

Export Citation Format

Share Document