scholarly journals Review: NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 503 ◽  
Author(s):  
Cattaneo ◽  
Stellari
Keyword(s):  
Chemical Composition ◽  
Process Control ◽  
Water Absorption ◽  
Near Infrared ◽  
Raw Materials ◽  
Nir Spectroscopy ◽  
Innovative Approach ◽  
Different Types ◽  
Key Issues ◽  
Horticultural Products

The last 10 years of knowledge on near infrared (NIR) applications in the horticultural field are summarized. NIR spectroscopy is considered one of the most suitable technologies of investigation worldwide used as a nondestructive approach to monitoring raw materials and products in several fields. There are different types of approaches that can be employed for the study of key issues for horticultural products. In this paper, an update of the information collected from the main specific International Journals and Symposia was reported. Many papers showed the use of NIR spectroscopy in the horticultural field, and the literature data were grouped per year, per product, and per application, such as studies of direct (chemical composition) and indirect (physical and sensorial) properties (P), process control (PC), and authenticity and classification studies (AC). A mention was made of a recent innovative approach that considers the contribution of water absorption in the study of biological systems.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Industrial Applications of Near-Infrared Chemical Imaging Microscopy

2001 ◽  
Vol 7 (S2) ◽  
pp. 162-163
Author(s):  
EN Lewis ◽  
LH Kidder ◽  
KS Haber
Keyword(s):  
Spatial Distribution ◽  
Near Infrared ◽  
Imaging System ◽  
Raw Materials ◽  
Single Point ◽  
Nir Spectroscopy ◽  
Chemical Imaging ◽  
Industrial Applications ◽  
Chemical Information ◽  
Spatially Resolved

Single point near-infrared (NIR) spectroscopy is used extensively for characterizing raw materials and finished products in a wide variety of industries: polymers, paper, film, pharmaceuticals, paintings and coatings, food and beverages, agricultural products. As advanced industrial materials become more complex, their functionality is often determined by the spatial distribution of their discrete sample constituents. However, conventional single point NIR spectroscopy cannot adequately probe the interrelationship between the spatial distribution of sample components with the physical properties of the sample. to fully characterize these samples, it is necessary to probe simultaneously spatial and chemical heterogeneity and correlate these properties with sample characteristics.Recently, we have developed a novel NIR imaging spectrometer that can deliver spatially resolved chemical information very rapidly. in contrast to conventional, single point NIR spectrometers, the imaging system uses an infrared focal-plane array (FPA) to collect up to 76,800 complete spectra, one for each pixel on the array, in approximately one minute.

scholarly journals Ability of NIR spectroscopy to predict meat chemical composition and quality – a review

2011 ◽  
Vol 49 (No. 11) ◽  
pp. 500-510 ◽  
Author(s):  
M. Prevolnik ◽  
M. Čandek-Potokar ◽  
D. Škorjanc
Keyword(s):  
Chemical Composition ◽  
Meat Quality ◽  
Quality Evaluation ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
The Other ◽  
Meat Composition ◽  
Feeding Regimes ◽  
Simultaneous Assessment

In contrast to conventional methods for the determination of meat chemical composition and quality, near infrared spectroscopy (NIRS) enables rapid, simple and simultaneous assessment of numerous meat properties. The present article is a review of published studies that examined the ability of NIRS to predict different meat properties. According to the published results, NIRS shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. On the other hand, NIRS is less accurate for predicting different attributes of meat quality. In view of meat quality evaluation, the use of NIRS appears more promising when categorizing meat into quality classes on the basis of meat quality traits for example discriminating between feeding regimes, discriminating fresh from frozen-thawed meat, discriminating strains, etc. The performance of NIRS to predict meat properties seems limited by the reliability of the method to which it is calibrated. Moreover, the use of NIRS may also be limited by the fact that it needs a laborious calibration for every purpose. In spite of that, NIRS is considered to be a very promising method for rapid meat evaluation.    

scholarly journals From the Laboratory to The Vineyard—Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis

2019 ◽  
Vol 8 (4) ◽  
pp. 21
Author(s):  
Aoife Power ◽  
Vi Khanh Truong ◽  
James Chapman ◽  
Daniel Cozzolino
Keyword(s):  
Chemical Composition ◽  
Hyperspectral Imaging ◽  
High Throughput ◽  
Analytical Methods ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compounds ◽  
Spectroscopic Techniques ◽  
High Throughput Analysis ◽  
Laboratory Methods

Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectral imaging) provide analysts with an innovative and improved understanding of complex issues by determining several chemical compounds and metabolites at once, allowing for the collection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition.

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.

Rapid analysis of chemical composition in intact and milled rice cookies using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 330-337 ◽  
Author(s):  
Latthika Wimonsiri ◽  
Pitiporn Ritthiruangdej ◽  
Sumaporn Kasemsumran ◽  
Nantawan Therdthai ◽  
Wasaporn Chanput ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
Wheat Gluten ◽  
Nir Spectroscopy ◽  
Rice Flour ◽  
Coefficient Of Determination ◽  
Gluten Free ◽  
Least Squares Regression ◽  
Milled Rice ◽  
Control Programs

This study has investigated the potential of near infrared (NIR) spectroscopy to predict the content of moisture, protein, fat and gluten in rice cookies in different sample forms (intact and milled samples). Gluten-free (n = 48) and gluten (n = 48) rice cookies were formulated with brown and white rice flours in which butter was substituted with fat replacer at 0, 15, 30 and 45%. With regard to gluten cookies, rice flour was substituted with wheat gluten at 1, 3 and 5%. Partial least squares regression modeling produced models with coefficient of determination (R2) values greater than 0.88 from NIR spectra of intact samples and greater than 0.92 for milled samples. These models were able to predict the four components with a ratio of prediction to deviation greater than 2.7 and 3.8 in intact and milled samples, respectively. The results suggest that the models obtained from the intact samples can be successfully applied for chemical composition of rice cookies and are reliable enough use for potential quality control programs.

scholarly journals Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals

2016 ◽  
Vol 71 (3) ◽  
pp. 520-532 ◽  
Author(s):  
José A. Adame-Siles ◽  
Tom Fearn ◽  
José E. Guerrero-Ginel ◽  
Ana Garrido-Varo ◽  
Francisco Maroto-Molina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Animal Health ◽  
Real Life ◽  
Nir Spectroscopy ◽  
Geostatistical Analysis ◽  
Structure Characterization ◽  
Sampling Point ◽  
Fiber Optic Probe ◽  
Effective Manner

Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

Using NIR and ATR-FTIR spectroscopy to rapidly detect compression wood inPinus radiata

2014 ◽  
Vol 44 (7) ◽  
pp. 820-830 ◽  
Author(s):  
J. Paul McLean ◽  
Guangwu Jin ◽  
Maree Brennan ◽  
Michél K. Nieuwoudt ◽  
Philip J. Harris
Keyword(s):  
Chemical Composition ◽  
Compression Wood ◽  
Near Infrared ◽  
Water Saturation ◽  
Nir Spectroscopy ◽  
Analytical Techniques ◽  
Gradual Change ◽  
Least Squares Regression ◽  
Dimensional Changes ◽  
Discriminatory Analysis

Compression wood has undesirable properties for structural timber and for paper production. Traditional methods of detecting it are often time consuming and subjective. This study aimed to rapidly and impartially detect compression wood through the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and diffuse reflectance near infrared (NIR) spectroscopy. Compression wood and non-compression wood samples were obtained from young Pinus radiata D. Don trees grown in New Zealand. Longitudinal dimensional changes were measured during drying or water saturation of the samples; lignin and galactose contents were determined using conventional analytical techniques. Chemical composition was here a more reliable discriminator between wood types than longitudinal dimensional changes. It was shown that partial least-squares regression (PLS-R) or discriminatory analysis (PLS-DA) could be used to build models on the training samples that could discriminate between wood types of the independently grown validation samples. Ultimately, both types of spectroscopies could be used to discriminate between compression wood and non-compression wood either through prediction or discriminatory analysis with equal success. Investigation into spectral differences between wood types, including sequential mixtures of wood types, showed that for the mid-IR region absorbance at a well-resolved lignin band could be used to discriminate compression wood from non-compression wood. For NIR, a similar investigation showed that absorbance values at four separate wavenumbers or the 6000–5600 cm−1region of the first derivative spectra were required for that discrimination. It is proposed that if there is a gradual change in the chemical composition of compression wood with its severity, then IR spectroscopy could feasibly be used to rapidly determine compression wood severity.

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