Prediction of Water Activity in Mamón (Filipino sponge) Cakes by near Infrared Hyperspectral Imaging

2020 ◽  
Vol 862 ◽  
pp. 7-11
Author(s):  
Manunchaya Sricharoonratana ◽  
Sontisuk Teerachaichayut
Keyword(s):  
Hyperspectral Imaging ◽  
Water Activity ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
The Philippines ◽  
Activity Measurement ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Physical And Chemical ◽  
Non Destructive

Water activity in foods can result in detrimental microbial activity during storage. The usual methods of water activity measurement involve destruction of the sample. Near infrared (NIR) hyperspectral imaging has previously been successfully used as a non-destructive method to determine various physical and chemical characteristics of a variety of foods. Therefore, this method was tested to determine whether it could be used to measure water activity of mamón cakes, a popular sponge cake developed in the Philippines. Individual samples (n = 178) were divided into a calibration set (n=119) and a prediction set (n=59). These samples were tested using NIR hyperspectral imaging (935-1720 nm) with a smoothing spectral pretreatment selected for developing the calibration model. Partial least squares regression was used to establish the model in order to predict the water activity. The results showed the accuracy of the calibration model in prediction that gave a correlation coefficient of 0.767 and the root mean square error of prediction of 0.0130. It was therefore concluded that NIR hyperspectral imaging has a potential for use and application for measuring the water activity of mamón cakes.

scholarly journals The application of NIR spectroscopy in moisture determining of vegetable seeds

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 131-136
Author(s):  
Wojciech Poćwiardowski ◽  
Joanna Szulc ◽  
Grażyna Gozdecka
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Moisture Analysis ◽  
Non Destructive

The aim of the study was to elaborate a universal calibration for the near infrared (NIR) spectrophotometer to determine the moisture of various kinds of vegetable seeds. The research was conducted on the seeds of 5 types of vegetables – carrot, parsley, lettuce, radish and beetroot. For the spectra correlation with moisture values, the method of partial least squares regression (PLS) was used. The resulting qualitative indicators of a calibration model (R = 0.9968, Q = 0.8904) confirmed an excellent fit of the obtained calibration to the experimental data. As a result of the study, the possibilities of creating a calibration model for NIR spectrophotometer for non-destructive moisture analysis of various kinds of vegetable seeds was confirmed.<br /><br />

scholarly journals Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019 ◽  
Vol 9 (18) ◽  
pp. 3926 ◽  
Author(s):  
Yue Zhang ◽  
Hongzhe Jiang ◽  
Wei Wang
Keyword(s):  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chicken Meat ◽  
Regions Of Interest ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Distribution Maps ◽  
Plsr Model ◽  
Volume Range

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

Optimization of Wheat Protein Near-Infrared Calibration Model Based on SPXY

2013 ◽  
Vol 803 ◽  
pp. 122-126 ◽  
Author(s):  
Xiao Dong Mao ◽  
Lai Jun Sun ◽  
Gang Hao ◽  
Lu Lu Xu ◽  
Guang Yan Hui
Keyword(s):  
Spectral Data ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Calibration Model ◽  
Spectral Features ◽  
Mean Square ◽  
Wheat Protein ◽  
Least Squares Regression ◽  
Training Set ◽  
Real Samples

It is very crucial that arepresentative training set can be extracted from a pool of real samples. Inthis paper, a representative set of correction samples of wheat protein contentis selected by using SPXY algorithm firstly; Secondly, the spectral data ispretreated to enhance spectral features; Thirdly, the model of wheat grainprotein is established by using partial least squares regression. The resultsshow that the model established by the calibration set selected by SPXY isbetter than the model established by the calibration set selected randomly.Root mean square error of prediction(RMSEP) and prediction correlationcoefficient(R) are 0.41094 and 0.97705 respectively, which are similar to themodel established by the initial calibration set.

scholarly journals Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Zhengyan Xia ◽  
Chu Zhang ◽  
Haiyong Weng ◽  
Pengcheng Nie ◽  
Yong He
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Selection Methods ◽  
Wavelength Selection ◽  
Ophiopogon Japonicus

Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.

Developing and testing a new quantitative near infrared spectroscopy online tracking measuring system for soil detection during automatic dishwashing

2021 ◽  
pp. 096703352110066
Author(s):  
Johannes Richter ◽  
Arnd Kessler ◽  
Thomas Weber ◽  
Heinz Heißler ◽  
Michaela Gerstenlauer ◽  
...  
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Egg Yolk ◽  
Measuring System ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Automatic Dishwashing ◽  
Soil Component ◽  
Online Tracking ◽  
Soil Components

Near infrared (NIR) measurements have been used for several years to examine the processes taking place in the dishwasher during dishwashing. It is possible to differentiate between the soil components butterfat, oatmeal and egg-yolk and to determine their concentration in the dishwashing liquor quantitatively. Consequently, time-consuming dishwashing tests can be avoided by weighing the dishes. However, this method is also based on a small number of NIR measurements which are carried out intrusively during the dishwashing process, i.e. outside the dishwasher. These few NIR measurements make it difficult to investigate the dynamics of a dishwashing process. In this study, the development, testing and usage of a new online tracking measuring system is presented. The latter was used to perform 38 dishwashing processes, each containing 51 NIR spectra, to develop a calibration model using the partial least squares regression method with cross-validation. This new online tracking measuring system, based on the calibration, can determine the concentrations of three different soil components in the dishwashing liquor during automatic dishwashing. By recording the 51 spectra, it is possible to display a tracking curve for each soil component, i.e. the concentration courses of the dishwashing process over time. This results in a significantly better time resolution and it was possible to investigate the first dynamic part of the tracking curve, i.e. the beginning of the dishwashing process. This could lead to the opportunity to change the state of the dishwasher depending on the concentrations detected in the first step and, secondly, to a more environmentally friendly and cost-reducing dishwashing process.

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