Feasibility of portable NIR spectrometer for quality assurance in glue-laminated timber production

Sandak, Jakub; Niemz, Peter; Hänsel, Andreas; Mai, Juana; Sandak, Anna

doi:10.1016/j.conbuildmat.2021.125026

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…Off-line determination of diverse wood quality aspects by a portable NIR sensor was demonstrated by Sandak et al in glue-laminated timber. The chemometric model specifically developed was demonstrated to be helpful in prediction of the total delamination and detailed delamination length ( Sandak et al, 2021 ).…”

Section: Applications To Wood Cellulose Lignin and Lignitementioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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Section: Applications To Wood Cellulose Lignin and Lignitementioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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“…Alkaline reagents facilitate the breakdown of lignin into smaller, low-molecular-weight compounds [61,82]. An observable peak split was noticed around 1064 cm −1 in the raw BP due to O-H vibrations [83,84]. A glitch noted at 847 cm −1 in the raw BP has been unseen in the treated BP.…”

Section: Ftir Analysismentioning

confidence: 99%

Investigating the Mechanical, Thermal, and Crystalline Properties of Raw and Potassium Hydroxide Treated Butea Parviflora Fibers for Green Polymer Composites

Mohan,

Priya,

Arunachalam

et al. 2023

Polymers

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The only biotic factor that can satisfy the needs of human species are plants. In order to minimize plastic usage and spread an immediate require of environmental awareness, the globe urges for the development of green composite materials. Natural fibers show good renewability and sustainability and are hence utilized as reinforcements in polymer matrix composites. The present work concerns on the usage of Butea parviflora fiber (BP), a green material, for high end applications. The study throws light upon the characterization of raw and potassium hydroxide (KOH)–treated Butea Parviflora plant, where its physical, structural, morphological, mechanical, and thermal properties are analyzed using the powder XRD, FTIR spectroscopy, FESEM micrographs, tensile testing, Tg-DTA, Thermal conductivity, Chemical composition, and CHNS analysis. The density values of untreated and KOH-treated fibers are 1.238 g/cc and 1.340 g/cc, respectively. The crystallinity index of the treated fiber has significantly increased from 83.63% to 86.03%. The cellulose content of the treated fiber also experienced a substantial increase from 58.50% to 60.72%. Treated fibers exhibited a reduction in both hemicelluloses and wax content. Spectroscopic studies registered varying vibrations of functional groups residing on the fibers. SEM images distinguished specific changes on the raw and treated fiber surfaces. The Availability of elements Carbon, Nitrogen, and Hydrogen were analyzed using the CHNS studies. The tensile strength and modulus of treated fibers has risen to 192.97 MPa and 3.46 Gpa, respectively. Thermal conductivity (K) using Lee’s disc showed a decrement in the K values of alkalized BP. The activation energy Ea lies between 55.95 and 73.15 kJ/mol. The fibers can withstand a good temperature of up to 240 °C, presenting that it can be tuned in for making sustainable composites.

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“…IR spectroscopy is a commonly used spectroscopic technique, which is widely used for food quality testing [ 9 ], herb identification [ 10 ], and soil composition analysis [ 11 ]. Many machine learning methods have been proposed to extract the desired information from spectral data, including Naive Bayes (NB) [ 12 ], support vector machine (SVM) [ 13 , 14 ], random forest (RF) [ 15 , 16 ], K-nearest neighbor (KNN) [ 17 ], partial least squares (PLS) [ 18 , 19 ], etc. Since IR spectra introduce noise during the acquisition process, IR spectra contain not only the target information but also irrelevant information such as noise [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy

Sun

Wang

Dong

2023

Sensors

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Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN–LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN–LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN–LSTM neural network combined with IR spectroscopy helps to identify different pasta products.

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Feasibility of portable NIR spectrometer for quality assurance in glue-laminated timber production

Cited by 10 publications

References 44 publications

Portable NIR spectroscopy: the route to green analytical chemistry

Portable NIR spectroscopy: the route to green analytical chemistry

Investigating the Mechanical, Thermal, and Crystalline Properties of Raw and Potassium Hydroxide Treated Butea Parviflora Fibers for Green Polymer Composites

CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy

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