Fast online NIR technique to predict MOE and moisture content of sawn lumber

Abstract A fast online grading apparatus for sawn lumber based on near-infrared (NIR) spectroscopy has been developed. The method is based on a novel wavelength dispersive NIR spectrophotometer equipped with a diffraction grating linear sensor and high-intensity lighting. It was possible to acquire spectra from the entire surface of Hinoki cypress lumber sections traveling on a conveyor belt at a speed of 120 m min-1. Additionally, predictive models for moisture content (MC) and modulus of elasticity (MOE) under various MC conditions were developed from the NIR spectra with the aid of partial least squares regression (PLSR) analysis. Both the MC and MOE predictive models demonstrated sufficient levels of prediction accuracy for use on high-speed conveyor belts, and the results of various experiments indicate that the developed device could be applied for the online quality certification of sawn lumber in commercial sawmills.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033518811921 ◽

2018 ◽

Vol 27 (1) ◽

pp. 38-45 ◽

Cited By ~ 2

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.

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

Holzforschung ◽

10.1515/hf.2008.060 ◽

2008 ◽

Vol 62 (4) ◽

Cited By ~ 16

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.

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Estimating mechanical properties and specific gravity for five-year-old Eucalyptus tereticornis having broad moisture content range by NIR spectroscopy

Holzforschung ◽

10.1515/hf.2011.055 ◽

2011 ◽

Vol 65 (5) ◽

Cited By ~ 15

Author(s):

Vimal Kothiyal ◽

Aasheesh Raturi

Keyword(s):

Specific Gravity ◽

Moisture Content ◽

Near Infrared ◽

Wood Property ◽

Nir Spectroscopy ◽

Modulus Of Rupture ◽

Bending Test ◽

Coefficient Of Determination ◽

Eucalyptus Tereticornis ◽

Least Squares Regression

Abstract Near infrared spectroscopy coupled with multivariate data analysis has been used to predict the specific gravity, modulus of rupture, modulus of elasticity, and fiber stress at elastic limit in bending tests on radial and tangential strip wood samples obtained from five-year-old Eucalyptus tereticornis. Moisture content of samples was 6–21% for bending test and 7–16% for specific gravity. Partial least squares regression calibrations were developed for each wood property. Calibrations had good relationships between values measured in laboratory and NIR predicted values obtained from small clear samples. The coefficient of determination (R2) for calibration ranged from 0.76 to 0.83 and for prediction (Rp 2) it was between 0.58 and 0.77. Both radial and tangential faces are equally suited for calibration (for radial face R2 was 0.77–0.83 and for tangential it was 0.76–0.83). Standard errors of predictions were slightly higher compared to standard error of calibration.

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Spectral Range and Wood Surface Impacts on Equilibrium Moisture Content Estimation in Thermally Modified Beech Wood by FT-NIR Spectroscopy

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1208/1/012022 ◽

2021 ◽

Vol 1208 (1) ◽

pp. 012022

Author(s):

Nebojša Todorović

Keyword(s):

Moisture Content ◽

Equilibrium Moisture Content ◽

Cross Section ◽

Near Infrared ◽

Prediction Models ◽

Beech Wood ◽

Nir Spectroscopy ◽

Principal Component ◽

Least Squares Regression ◽

The Cross

Abstract Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares regression (PLS-R) were tested for the possibility of equilibrium moisture content (EMC) prediction in thermally modified beech wood (Fagus moesiaca C.). The samples were modified for 4h at temperatures of 170, 190 and 210 °C. After thermal modification, the samples were kept in a climatic chamber until EMC was reached. FT-NIR spectra (100 scans and 4 cm-1) were collected on the cross-section and radial surfaces at four points. PLS – R models were developed for four spectral regions: the first overtone, the second overtone, the third overtone and the combination band region. Applied thermal treatment caused a decrease of EMC by 42 % at 170 °C, by 53 % at 190 °C, and by 62 % at 210 °C. Principal component analysis (PCA) indicated that there is a difference both between treatments and between wood surfaces. The results of the spectra taken from the radial surface were, in all models, better than the spectra of the cross-section. Related to chemical changes, the first and second overtone region play an important role in the calibrations. The best prediction models for EMC of thermally modified beech wood were obtained from radial surface spectra in the first (Rp2=0.86, RPD=2.69) and second overtone region (Rp2=0.87, RPD=2.70). The obtain results could contribute to the development of predictive models in monitoring of EMC which could significantly improve the quality of industrial production of thermally modified wood.

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Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

10.5194/egusphere-egu21-3896 ◽

2021 ◽

Author(s):

Iva Hrelja ◽

Ivana Šestak ◽

Igor Bogunović

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Spectral Data ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Soil Samples ◽

Least Squares Regression ◽

Macro Scale ◽

Sentinel 2

Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.The study is performed in Mediterranean Croatia (44&#176; 05&#8217; N; 15&#176; 22&#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed Quercus ssp. and Juniperus ssp. forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22nd 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31st and September 5th 2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSEp) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSEp (Spectroscopy dataset RMSEp = 1.91; Sentinel-2 dataset RMSEp = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.Keywords: remote sensing, soil spectroscopy, wildfires, soil organic matterAcknowledgment: This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&#269;in is acknowledged for her cooperation during the laboratory work.

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Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

10.5194/egusphere-egu21-15021 ◽

2021 ◽

Author(s):

Hayfa Zayani ◽

Youssef Fouad ◽

Didier Michot ◽

Zeineb Kassouk ◽

Zohra Lili-Chabaane ◽

...

Keyword(s):

Organic Carbon ◽

Near Infrared ◽

Full Range ◽

Nir Spectroscopy ◽

Soil Samples ◽

Calibration Data ◽

Least Squares Regression ◽

Data Set ◽

Soil Carbon Stocks ◽

The Mean

Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40&#176;C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec&#174;3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation &#8710;SOCobsobtained from observed values in 2013 and 2018 (&#8710;SOCobs = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R&#178;= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed &#8710;SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which &#8710;SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/09670335211057235 ◽

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.

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NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.300 ◽

2001 ◽

Vol 9 (2) ◽

pp. 133-139 ◽

Cited By ~ 8

Author(s):

L.G. Thygesen ◽

S.B. Engelsen ◽

M.H. Madsen ◽

O.B. Sørensen

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Potato Starch ◽

Nir Spectroscopy ◽

Peak Time ◽

Phosphate Content ◽

Least Squares Regression ◽

Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

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Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

Canadian Journal of Forest Research ◽

10.1139/x03-189 ◽

2004 ◽

Vol 34 (1) ◽

pp. 76-84 ◽

Cited By ~ 17

Author(s):

Mulualem Tigabu ◽

Per Christer Odén ◽

Tong Yun Shen

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Least Squares Regression ◽

Absorption Bands ◽

Classification Rate ◽

Data Set ◽

Storage Lipids ◽

Nir Absorption ◽

Seed Origin ◽

Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

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The application of NIR spectroscopy in moisture determining of vegetable seeds

Czech Journal of Food Sciences ◽

10.17221/57/2019-cjfs ◽

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.

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