Analysis of Black Wattle (Acacia mearnsii De Wild) Bark by Near Infrared Spectroscopy

Holzforschung ◽  
2003 ◽  
Vol 57 (5) ◽  
pp. 527-532 ◽  
Author(s):  
L. R. Schimleck ◽  
Y. Yazaki
Keyword(s):  
Near Infrared ◽  
Rapid Assessment ◽  
Nir Spectroscopy ◽  
Hot Water ◽  
Soluble Solids ◽  
Tannin Content ◽  
Acacia Mearnsii ◽  
Considerable Potential ◽  
Black Wattle

Summary The analysis of two sets of Acacia mearnsii De Wild (Black Wattle) samples by near infrared (NIR) spectroscopy is reported. Set 1 samples were characterised in terms of hot water extractives, Stiasny value and polyflavanoid content. Set 2 samples were characterised by nine different parameters, including tannin content. NIR spectra were obtained from the milled bark of all samples and calibrations developed for each parameter. Calibrations developed for hot water extractives and polyflavanoid content (set 1) gave very good coefficients of determination (R2) and performed well in prediction. Set 2 calibrations were generally good with total and soluble solids, tannin content, Stiasny value-2 and UV-2, all having R2 greater than 0.8. Owing to the small number of set 2 samples, no predictions were made using the calibrations. The strong relationships obtained for many parameters in this study indicates that NIR spectroscopy has considerable potential for the rapid assessment of the quality of extractives in A. mearnsii bark.

scholarly journals Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Kirsti Cura ◽  
Niko Rintala ◽  
Taina Kamppuri ◽  
Eetta Saarimäki ◽  
Pirjo Heikkilä
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Automated Recognition ◽  
Textile Materials ◽  
Input Material ◽  
Adequate Quality ◽  
Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

Rapid Determination of Heartwood Extractives in Larix sp. by Means of Fourier Transform near Infrared Spectroscopy

2002 ◽  
Vol 10 (3) ◽  
pp. 203-214 ◽  
Author(s):  
N. Gierlinger ◽  
M. Schwanninger ◽  
B. Hinterstoisser ◽  
R. Wimmer
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Hot Water ◽  
Extractive Content ◽  
Fast Method ◽  
Wood Powder ◽  
Test Set

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy to rapidly determine extractive and phenolic content in heartwood of larch trees ( Larix decidua MILL., L. leptolepis (LAMB.) CARR. and the hybrid L. x eurolepis) was investigated. FT-NIR spectra were collected from wood powder and solid wood using a fibre-optic probe. Partial Least Squares (PLS) regression analyses were carried out describing relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. Besides cross and test set validation the established models were subjected to a further evaluation step by means of additional wood samples with unknown extractive content. Extractive and phenol contents of these additional samples were predicted and outliers detected through Mahalanobis distance calculations. Models based on the whole spectral range and without data pre-processing performed well in cross-validation and test set validation, but failed in the evaluation test, which is based on spectral outlier detection. But selection of data pre-processing methods and manual as well as automatic restriction of wavenumber ranges considerably improved the model predictability. High coefficients of determination ( R2) and low root mean square errors of cross-validation ( RMSECV) were obtained for hot water extractives ( R2 = 0.96, RMSECV = 0.86%, range = 4.9–20.4%), acetone extractives ( R2 = 0.86, RMSECV = 0.32%, range = 0.8–3.6%) and phenolic substances ( R2 = 0.98, RMSECV = 0.21%, range = 0.7–4.9%) from wood powder. The models derived from wood powder spectra were more precise than those obtained from solid wood strips. Overall, NIR spectroscopy has proven to be an easy to facilitate, reliable, accurate and fast method for non-destructive wood extractive determination.

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Short Wave ◽  
Least Squares Regression ◽  
Machine Learning Methods ◽  
Transmission Geometry ◽  
Modelling Techniques ◽  
Test Sets ◽  
Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

Near infrared (NIR)-spectroscopy and in-vitro dissolution absorption system 2 (IDAS2) can help detect changes in the quality of generic drugs

2020 ◽  
Vol 46 (1) ◽  
pp. 80-90
Author(s):  
Carlos Jiménez-Romero ◽  
Johayra Simithy ◽  
Anthony Severdia ◽  
Daniel Álvarez ◽  
Manuel Grosso ◽  
...  
Keyword(s):  
Near Infrared ◽  
Generic Drugs ◽  
Nir Spectroscopy ◽  
In Vitro Dissolution ◽  
Absorption System ◽  
System 2

Evaluation of Soya Bean Quality for Tofu Processing by near Infrared Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A325-A328
Author(s):  
T.L. Hong ◽  
Samson C.S. Tsou ◽  
S.-J. Tsai
Keyword(s):  
Water Absorption ◽  
Absorption Rate ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soya Bean ◽  
Control Measures ◽  
Raw Material ◽  
Prolonged Storage ◽  
Tannin Content ◽  
Water Absorption Rate

Soya bean, as the raw material for tofu processing, is required to be of high quality. The variety characteristics, storage conditions and harvesting seasons of soya bean are the major contributors to soya bean quality. This study attempted to use near infrared (NIR) spectroscopy to evaluate the processing quality of soya bean. Evaluation models using NIR spectroscopy were developed for the analyses of tannin content, degrees of lipid oxidation, detection of harvest seasons and measurement of water absorption rate. Simulation experiments demonstrated that these models were not only able to analyse major compositions of soya bean, but also to sort out soya bean samples and their suitability for tofu making regardless of various defects, such as high tannin content, low water absorption rate, prolonged storage and unfavourable harvest seasons. Statistic analysis suggested that these models could be used as mass-screening techniques for breeding programmes and quality control measures in tofu-processing factories.

Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy

2006 ◽  
Vol 57 (4) ◽  
pp. 403 ◽  
Author(s):  
Robert L. Long ◽  
Kerry B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Quality Standard ◽  
Sampling Procedure ◽  
Performance Model ◽  
Soluble Solids ◽  
Fruit Storage ◽  
Total Soluble Solids ◽  
Fruit Maturity

The imposition of a minimum total soluble solids (TSS) value as a quality standard for orange-flesh netted melon fruit (Cucumis melo L. reticulatus group) requires either a batch sampling procedure (i.e. the estimation of the mean and standard deviation of a population), or the individual assessment of fruit [e.g. using a non-destructive procedure such as near infrared (NIR) spectroscopy]. Several potential limitations to the NIR assessment of fruit, including the variation in TSS within fruit and the effect of fruit storage conditions on the robustness of calibration models, were considered in this study. Outer mesocarp TSS was 3 TSS units higher at the stylar end of the fruit compared with the stem end, and the TSS of inner mesocarp was higher than outer tissue and more uniform across spatial positions. The linear relationship between the outer 10 mm and the subsequent middle 10 mm of tissue varied with fruit maturity [e.g. 42 days before harvest (DBH), r 2 = 0.8; 13 DBH, r 2 = 0.4; 0 DBH, r 2 = 0.7], and with cultivars (at fruit maturity, Eastern Star 2001 r 2 = 0.88; Malibu 2001 r 2 = 0.59). This relationship notably affected NIR calibration performance (e.g. based on inner mesocarp TSS; R c 2 = 0.80, root mean standard error of cross-validation (RMSECV) = 0.65, and R c 2 = 0.41, RMSECV = 0.88 for mature Eastern Star and Malibu fruit, respectively). Cold storage of fruit (0–14 days at 5°C) did not affect NIR model performance. Model performance was equivalent when based on either that part of the fruit in contact with the ground or equatorial positions; however, it was improved when based on the stylar end of the fruit.

scholarly journals Methods of overcoming dormancy in seeds and initial development of Black Wattle

2020 ◽  
Vol 42 ◽  
pp. e44
Author(s):  
Ricardo Alberti ◽  
Fernanda Lais Novello ◽  
Eduardo Alexandre Albarello ◽  
Ewerton Jose de Medeiros Torres ◽  
Fernando De Jesus Moreira Junior
Keyword(s):  
Sulfuric Acid ◽  
Aerial Part ◽  
Hot Water ◽  
Forest Nursery ◽  
Acacia Mearnsii ◽  
Initial Development ◽  
Efficient Treatment ◽  
Running Water ◽  
Concentrated Sulfuric Acid ◽  
Black Wattle

The objective of this work was to analyze the efficacy of different methods of dormancy break in seeds of Acacia mearnsii De Wild. The work was performed at the UFSM Botany Lab and Forest Nursery, in Frederico Westphalen, RS. The treatments were: (T1) control; (T2) immersion in hot water (90 °C) for 5 minutes, followed by rest; (T3) immersion in hot water (90 °C) for 10 minutes, followed by rest; (T4) chemical scarification with concentrated sulfuric acid for 3 minutes, followed by washing under running water; (T5) chiseling with concentrated sulfuric acid for 5 minutes, followed by washing under running water. The seeds were sown in plastic trays with Oxisol and vermiculite. The results were submitted to analysis of variance and data were differentiated by Test t with 5% of probability. The most efficient treatment to overcome dormancy was T2, presenting 81.29% of germinated seeds and IVG of 18.23 days for germination. This treatment was also the best for the variables of physical vigor and best performing root length and aerial part, and did not differ from improved treatments for diameter and green mass weight.

scholarly journals Soluble Solids and Simple Sugars Measurement in Intact Mango Using Near Infrared Spectroscopy

2008 ◽  
Vol 18 (3) ◽  
pp. 410-416 ◽  
Author(s):  
Stephen R. Delwiche ◽  
Weena Mekwatanakarn ◽  
Chien Y. Wang
Keyword(s):  
Standard Error ◽  
Quality Evaluation ◽  
Near Infrared ◽  
Wavelength Region ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Different Temperatures ◽  
The Individual

A rapid, reliable, and nondestructive method for quality evaluation of mango (Magnifera indica) fruit is important to the mango industry for international trade. The objective of this study was to determine the potential of near-infrared (NIR) spectroscopy to predict soluble solids content (SSC) and individual and combined concentrations of sucrose, glucose, and fructose nondestructively in mango. Mature mangoes at two different temperatures (15 °C and 20 °C) were measured by NIR interactance (750–1088 nm wavelength region analyzed) over an 11-day period, starting when the fruit were underripe and extending to a few days past optimal ripeness. Partial least squares regression was used to develop models for SSC, individual sugar concentration, and the sum of the concentrations of the three sugars. Such analyses yielded calibration equations with R2 = 0.77 to 0.88 (SSC), 0.75 (sucrose), 0.67 (glucose), 0.70 (fructose), and 0.82 (sum); standard error of calibration = 0.56 to 0.90 (SSC), 10.0 (sucrose), 0.9 (glucose), 4.5 (fructose), and 10.4 (sum); and standard error of cross-validation = 0.93 to 1.10 (SSC), 15.6 (sucrose), 1.4 (glucose), 6.9 (fructose), and 16.8 (sum). When the SSC calibration was applied to a separate validation set, the standard error of performance ranged from 0.94% to 1.72%. These results suggest that for assessment of mango ripeness, NIR SSC calibrations are superior to the NIR calibrations for any of the individual sugars. This nondestructive technology can be used in the screening and grading of mangoes and in quality evaluation at wholesale and retail levels.

scholarly journals Overall precision test of near infrared spectroscopy on mango fruits (Mangifera indica CV. ‘Nam Dok Mai Si Thong’) by on-line and off-line systems

2020 ◽  
Vol 187 ◽  
pp. 04006
Author(s):  
Wachiraya Lekhawattana ◽  
Panmanas Sirisomboon
Keyword(s):  
Near Infrared ◽  
Mangifera Indica ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Online Systems ◽  
On Line ◽  
Line Scanning ◽  
Brix Value ◽  
Precision Test

The near infrared (NIR) spectroscopy both on-line and off-line scanning was applied on mango fruits (Mangifera indica CV. ‘Nam dok mai- si Thong’) for the overall precision test. The reference parameter was total soluble solids content (Brix value). The results showed that the off-line scanning had a higher accuracy than on-line scanning. The scanning repeatability of the off-line and on-line systems were 0.00199 and 0.00993, respectively. The scanning reproducibility of the off-line and online systems were 0.00279 and 0.00513, respectively. The reference of measurement repeatability was 0.2. The maximum coefficient of determination (R2max) of the reference measurement was 0.894.

scholarly journals Implementation of Non Destructive FTNIR Method for Quick Estimation of Peanut Quality Based on FFA and Peroxide Value

2021 ◽  
Author(s):  
Rakesh Kumar Kumar Raigar ◽  
Shubhangi Srivast ◽  
Hari Niwas Mishra
Keyword(s):  
Peroxide Value ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Model Building ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Calibration Model ◽  
Peanut Kernel ◽  
Non Destructive

Abstract The possibility of rapid estimation of moisture, protein, fat, free fatty acid (FFA), and peroxide value (PV) content in peanut kernel was studied by Fourier transform near-infrared spectroscopy (FTNIR) in the diffuse reflectance mode along with chemometric technic. The moisture, fat and protein of fresh and damaged seeds of peanuts ranging from 3 to 9 %, 45 to 57 % and 23 to 27 % respectively, were used for the calibration model building based on partial least squares (PLS) regression. The peanut samples had major peaks at wavenumbers 53.0853, 4954.98, 4464.03, 4070.85, 74.75.63, 8230.21, and 6178.13 in per cm. First and second derivate mathematical preprocessing was also applied in order to eliminate multiple baselines for different chemical quality parameters of peanut. The FFA had the lowest value of calibration and validation errors (0.579 and 0.738) followed by the protein (0.736 and 0.765). The quality of peanut seeds with lowest root mean square error of cross validation of 0.76 and maximum correlation coefficient (R2) of 96.8 was obtained. The comprehensive results signify that FT-NIR spectroscopy can be used for rapid, non-destructive quantification of quality parameters in peanuts.

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