scholarly journals Quality Control of Honey Using New Generation Infrared Spectrometers

2017 ◽  
Vol 5 (4) ◽  
pp. 326 ◽  
Author(s):  
Huseyin Ayvaz
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Quality Parameters ◽  
Handheld Devices ◽  
Least Squares Regression ◽  
Free Acidity ◽  
Regulatory Limits ◽  
Wide Range ◽  
New Generation

The objective of this study was to develop a rapid infrared technique to determine 10 key quality parameters (sucrose, glucose, fructose, reducing sugar, 5-HMF, °Brix, moisture content, water activity, pH and free acidity) in honey by using new generation portable and handheld devices. The composition of honey samples (n=59) collected from different parts of Turkey was analyzed by using established reference methods, giving wide range of concentrations for each parameter. The levels of sucrose and 5-HMF in some samples were above the established regulatory limits (Codex Alimentarius and European Union standards), indicating possible adulteration or process and storage abuse. Spectra were collected by using portable Fourier-Transformed infrared (FTIR) and handheld NIR (Near Infrared) spectrometers. Partial least squares regression (PLSR) approach was used to correlate the spectral features with compositional reference values, giving strong linear correlation coefficients and standard errors of prediction. Although both systems performed similarly, portable FTIR system was superior in predictions of sucrose, 5-HMF and free acidity while portable NIR system performed noticeably better for °Brix and moisture content. The data indicates that all of the 10 parameters can be measured within the minutes using both systems, providing reliable screening capabilities, flexibility and the potential for in-field applications.

scholarly journals Application of visible-near infrared spectroscopy to evaluate the quality of button mushrooms

2018 ◽  
Vol 27 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Valentina Giovenzana ◽  
Alessio Tugnolo ◽  
Andrea Casson ◽  
Riccardo Guidetti ◽  
Roberto Beghi
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Distribution Channel ◽  
Nir Spectroscopy ◽  
Quality Standard ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Physical Damage ◽  
Reference Quality

The Agaricus bisporus mushroom is one of the most cultivated and consumed mushrooms in the world, thanks to its delicacy, nutritional value and flavour. The quality evaluation of the A. bisporus during the harvest is generally established by a visual check by trained operators. This method complies with the request of the Distribution Channel (DC) to retailers and guarantees very low physical damage to the mushrooms; nevertheless, it is subjective and it does not guarantee the highest quality standard for the consumer. The aim of this study was to test the use of visible/near infrared (vis/NIR) reflectance spectroscopy (400–1000 nm) to objectively evaluate the quality parameters of A. bisporus mushrooms. A total of 167 samples of A. bisporus mushrooms were harvested according to the main DC purchasing standards. The vis/NIR analyses were performed the day of sampling just before the physico-chemical analyses (sizes, firmness, soluble solids content and moisture content) used as reference quality parameters. The vis/NIR spectra were correlated to reference measures in order to build predictive models using the partial least squares regression method. Calculated models gave positive results regarding the prediction of the moisture content (r2(pred) = 0.78) and firmness (r2(pred) = 0.78). Results of this explorative study could be considered encouraging and demonstrate the applicability of vis/NIR spectroscopy on A. bisporus as a rapid technique (i) to monitor the productive process directly at the company, (ii) to standardize the harvest moment, and (iii) to support DC’s buyers’ choices, nowadays exclusively based on product external characteristics.

Prediction of Gasoline Properties with near Infrared Spectroscopy

1998 ◽  
Vol 6 (1) ◽  
pp. 341-348 ◽  
Author(s):  
Gy. Bohács ◽  
Z. Ovádi ◽  
A. Salgó
Keyword(s):  
Octane Number ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Correlation Coefficients ◽  
Quality Parameters ◽  
Standard Test ◽  
Poor Correlation ◽  
Test Procedures ◽  
Special Equipment ◽  
Wide Range

The test measurements used for the analysis of gasoline quality are mostly complicated standard procedures which are time consuming and which require special equipment, large volume of samples and specialists. The standard test methods could be partly replaced with non-destructive near infrared (NIR) spectroscopic measurements which are fast and less expensive. The aim of this paper is to present a feasible procedure for the prediction of quality parameters of gasoline from its NIR spectrum in a large and very diverse sample set. 350 commercially available gasoline samples were collected from July 1996. The samples covered summer and winter grades of normal, super and superplus unleaded gasolines with minimum RON requirements of 91, 95 and 98, respectively. These fuels covered a wide range of samples from very different sources including Hungarian and foreign refineries and pumps. An InfraPrime Lab Analyser (Bran+Luebbe) with high quality optical fibres in combination with multivariate calibration (PLSR) was used to determine 12 different chemical and physical properties of gasolines including reseach octane number (RON), motor octane number (MON), benzene, methyl-tertier-buthyl-ether (MTBE), sulphur content, distillation characteristics, Reid vapour pressure (RVP) and density at 15°C. The developed NIR methods predicted four important gasoline properties (RON, MON, benzene and MTBE content) with reproducibilities equivalent to the standard test procedures. The standard errors of prediction were 0.34 for RON, 0.30 for MON, 0.13%(vv−1) for benzene and 0.21%(vv−1) for MTBE content. The correlation coefficients were better than 0.970 in these calibrations. Calibrations developed for other gasoline properties showed poor correlation coefficients and allowed each parameter to be predicted only with higher standard error than the reference values. The NIR methods described are suitable for routine selection measurements in large series of gasoline samples.

scholarly journals Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8222
Author(s):  
Olga Escuredo ◽  
Laura Meno ◽  
María Shantal Rodríguez-Flores ◽  
Maria Carmen Seijo
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Reducing Sugars ◽  
Quality Parameters ◽  
Portable Devices ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Potato Quality

The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

scholarly journals Quantitative prediction of moisture content distribution in acetylated wood using near-infrared hyperspectral imaging

2022 ◽  
Author(s):  
Muhammad Awais ◽  
Michael Altgen ◽  
Mikko Mäkelä ◽  
Tiina Belt ◽  
Lauri Rautkari
Keyword(s):  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Infrared Image ◽  
Moisture Distribution ◽  
Quantitative Prediction ◽  
Chemical Information ◽  
Least Squares Regression ◽  
Dynamic Vapor Sorption ◽  
Wood Surfaces

AbstractThe uptake of moisture severely affects the properties of wood in service applications. Even local moisture content variations may be critical, but such variations are typically not detected by traditional methods to quantify the moisture content of the wood. In this study, we used near-infrared hyperspectral imaging to predict the moisture distribution on wood surfaces at the macroscale. A broad range of wood moisture contents were generated by controlling the acetylation degree of wood and the relative humidity during sample conditioning. Near-infrared image spectra were then measured from the surfaces of the conditioned wood samples, and a principal component analysis was applied to separate the useful chemical information from the spectral data. Moreover, a partial least squares regression model was developed to predict moisture content on the wood surfaces. The results show that hyperspectral near-infrared image regression can accurately predict the variations in moisture content across wood surfaces. In addition to sample-to-sample variation in moisture content, our results also revealed differences in the moisture content between earlywood and latewood in acetylated wood. This was in line with our recent studies where we found that thin-walled earlywood cells are acetylated faster than the thicker latewood cells, which decreases the moisture uptake during the conditioning. Dynamic vapor sorption isotherms validated the differences in moisture content within earlywood and latewood cells. Overall, our results demonstrate the capabilities of hyperspectral imaging for process analytics in the modern wood industry. Graphical abstract

Estimation of Crystallinity of Poplar Cellulose Using Near Infrared Spectroscopy

2012 ◽  
Vol 622-623 ◽  
pp. 1532-1535
Author(s):  
Zhen Bo Liu ◽  
Wen Yang Kong ◽  
Yi Xing Liu ◽  
Zhan Chuan Xue ◽  
Xiao Yan Shen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Least Squares Regression ◽  
Cellulose Crystallinity ◽  
Test Models

Many studies have successfully applied near infrared (NIR) spectroscopy to estimate important wood properties. In this paper, the use of NIR (350–2500 nm) spectroscopy to predict the cellulose crystallinity of Poplar (Populus nigra var.) was investigated. The calibration and test models were constructed using partial least squares regression (PLS). The correlations were significant both the calibration and the test samples using six factors, and the correlation coefficients (R2) were 0.9367, 0.9472 respectively. The results suggest that NIR spectroscope may provide a useful tool for rapid and accurate prediction of the cellulose crystallinity of Poplar.

scholarly journals Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Charles L. Y. Amuah ◽  
Ernest Teye ◽  
Francis Padi Lamptey ◽  
Kwasi Nyandey ◽  
Jerry Opoku-Ansah ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Multivariate Calibration ◽  
Principal Component ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Total Soluble Solids ◽  
Linear Discriminant ◽  
Identification Rate

The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

A survey of moisture distribution in two sets of Scots pine logs by NIR-spectroscopy

Holzforschung ◽  
2008 ◽  
Vol 62 (4) ◽  
Author(s):  
Kyösti Karttunen ◽  
Asta Leinonen ◽  
Matti-Paavo Sarén
Keyword(s):  
Moisture Content ◽  
Scots Pine ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Optical Probe ◽  
Moisture Distribution ◽  
Least Squares Regression ◽  
Step Procedure ◽  
Surface Drying ◽  
Automated Scanning

Abstract Moisture content distributions of Scots pine logs in the green state were measured by a novel multi-step procedure. After sample preparation, the transverse sections of the wood surfaces were scanned by an automated scanning device with a fiber optical probe connected to a Fourier transform near-infrared spectroscope. In the course of the measurement sequences, several issues were addressed, such as surface drying, measurement geometry, ease of automation and interconnected data handling. The near-infrared (NIR) data were first modeled separately for heartwood and sapwood by means of multivariate partial least squares regression. The models for moisture content were evaluated by root mean square error of prediction, the result being 0.8% for heartwood and 10% for sapwood. The two models were then applied to the NIR data collected from sets of disks cut from nine logs. The results of the calculated moisture contents were evaluated by methods of descriptive statistics, and they indicated clear differences and trends in the distribution of moisture content in transverse or longitudinal regions of a log. Additionally, inter-tree variation in moisture content was detected.

scholarly journals Direct Determination of Moisture in Powder Milk Using Near Infrared Spectroscopy

2006 ◽  
Vol 2006 ◽  
pp. 1-4 ◽  
Author(s):  
R. Nagarajan ◽  
Parul Singh ◽  
Ranjana Mehrotra
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Milk Powder ◽  
Direct Determination ◽  
Wavelength Region ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Karl Fischer

Moisture content in commercially available milk powder was investigated using near infrared (NIR) diffuse reflectance spectroscopy with an Indian low-cost dispersive NIR spectrophotometer. Different packets of milk powder of the same batch were procured from the market. Forty-five samples with moisture range 4–10% were prepared in the laboratory. Spectra of the samples were collected in the wavelength region 800–2500 nm. Moisture values of all the samples were simultaneously determined by Karl Fischer (KF) titration. These KF values were used as reference for developing calibration model using partial least squares regression (PLSR) method. The calibration and validation statistics areR cal2:0.9942,RMSEC:0.1040, andR val2:0.9822,RMSEV:0.1730. Five samples of unknown moisture contents were taken for NIR prediction using developed calibration model. The agreement between NIR predicted results and those of Karl Fischer values is appreciable. The result shows that the instrument can be successfully used for the determination of moisture content in milk powder.

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