Linking near-infrared spectroscopy of 25 tropical Gabonese hardwoods to tree ecological temperament

The current application and future potential of near infrared (NIR) spectroscopy in the evaluation of foods for domesticated animals and humans is enormous. Where used, NIR spectroscopy has revolutionized the analysis and nutritional evaluation of animal feeds and human foods by providing a rapid means of examination. The availability of accurate and rapid methods of evaluation is becoming increasingly important to meet the nutritional requirements of animals for meat, milk, wool and egg production. This is essential for efficient and economic animal production, to maintain animal health and to minimize environmental impact. Accurate evaluation methods are also needed in relation to national and international legislation that regulates the circulation, trade and inspection of foods and feeds, aids effective functioning of the market and guards the safety of animals and humans. The aim of this review is to outline the theory and principles of NIR spectroscopy and to focus primarily on its application in the field of animal nutrition. The vital role NIR spectroscopy is playing in the prediction of biologically meaningful feed characteristics, including data derived in vivo, is demonstrated particularly through its application to forage evaluation, but also in the examination of raw materials and compound feeds. While the applications of NIR spectroscopy to different foods and drinks are extensive, this review gives an overview only of selected reported applications including its use for predicting nutritive value (mainly water, protein, fat, sucrose and starch content), monitoring food processing and for food authentication. The review provides clear evidence that the future application of NIR spectroscopy will undoubtedly increase, playing a vital role in the authentication of the quality and origin of foods and feeds and enabling the complex methods of feed evaluation required in the future to be put into widespread use.

Science developments entered a new era in the 21 century. The tendency is clear. The individual scientific fields started to cross each other again in order to have the depth and width in the development. Especially the computer science, it is the fundamental pillar for all the others sciences nowadays. A good example is its applications in Near Infrared spectroscopy (NIR). NIR is a rather new molecular vibrational spectroscopy in the analytical science field. Because of its advantages of being suitable to measure both liquid and solid samples, non destructive to samples, quite acceptable instrumental S/N ratio and sensitivity, NIR technique can be used in many in-line or off-line quantitative analysis. Although NIR only started its development from the 80s of last century, this new technology is developing so quickly that NIR applications have already covered the field of agriculture, material manufacture and chemical industries, petroleum and chemical refining, pharmaceutics and cosmetics etc. It has been widely accepted by many industries, academics and R & D institutes all over the world. However, NIR spectroscopy is a secondary analytical technique. The development of NIR applications is completely dependent upon the good use of Chemometrics, which is a computerized mathematical modeling technique utilized in the analytical field. In the other words, the critical step of a successful NIR application is to make a good NIR calibration model. With a commercially available NIR instrument, to scan the samples and to obtain the NIR spectra are very simple. Furthermore, to use the instrument software to make a pseudo calibration model is also quite easy, but whether the model made will predict the concentration of the relevant components correctly in the unknown samples, is not guaranteed. In this paper, with some real analytical data sets, how to use the major fussy mathematical concepts, such as Principal Components Analysis (PCA), Partial List Square (PLS) etc., in modeling for various applications of NIR spectroscopy will be thoroughly reviewed and discussed.

Bayesian multiple response kernel regression model for high dimensional data and its practical applications in near infrared spectroscopy

The purpose of this article is to study risk and benefit in the application of near-infrared (NIR) spectroscopy to the coating process of granules to monitor the process for determining the coating end point. Cylindrical granules or spherical granules were used as core granules and were coated using a fluidized bed coating apparatus by spraying coating suspension. During the coating run, samples of granules were pulled at regular intervals and amount of talc or lactose, which were the components of the film layer, were estimated by NIR spectroscopy.When the coating layer of granules was thin like the case of the spherical granules, it was possible to monitor and understand the coating process well by an application of NIR spectroscopy, because it was possible to estimate some components in the coating layer simultaneously.However, it was found that as the coating layer became thick like the case of the cylindrical granules, NIR light was scattered by titanium dioxide in the coating layer, and that the increase of the coating layer estimated from NIR spectroscopy showed the misunderstanding saturation. NIR spectroscopy could not be used for the estimation of the granule coating process even if the formulation and amount applied for the coating was same as the spherical granules.When NIR spectroscopy is intended to be used to control granule coating process, it is necessary to check the impacts of the formulation applied for coating, the amount of the coating layer, and the thickness of the coating layer on the estimations.

On-line application of near infrared (NIR) spectroscopy in food production

Wood is a renewable and valuable resource for a variety of end-use application areas. However, rapid and reliable assessments are needed to identify the quality of the tree, timber or wood product at all stages of production and processing. The ideal technology for assessing wood and wood products must provide reliable data, be user-friendly, cost-competitive and provide a rapid analysis. The ultimate application of near infrared (NIR) spectroscopy of wood or wood products is to substitute for costly and time-consuming reference measurements in order to aid process optimisation or determine properties and genetic traits on large numbers of individual samples. Increased interest in the application of NIR spectroscopy in various research fields including wood is observed nowadays. A vast number of publications highlight the potential of NIR spectroscopy for the characterisation of wood in a broad area of uses. The Journal of Near Infrared Spectroscopy has published two special issues on the application of NIR to forestry and wood research in 2010 and 2011 and a recent literature search yielded in excess of 556,000 results which can be easily found by using the search terms “NIR” and “wood”. This mass of published data may suggest that the technique of NIR spectroscopy is widely understood and broadly adopted by the timber industry, but even in recent papers it is evident that there is still a need to better understand the fundamental issues regarding sample selection and preparation, instrument choice, correct measurement and spectral interpretation. In this paper we draw on more than 40 years of collective experience and summarise state-of-the-art knowledge regarding instrumentation, spectral acquisition and data mining in regard to wood science and technology. The goal of this tutorial is two-fold: first, to inform early career wood scientists of the critical steps in utilising NIR spectroscopy to assess the quality of wood. Second, to alert managers to the level of operator skill required for the successful adoption of NIR technology. Some basic information is presented here, but due to the limited size of the manuscript, reference to more specific and detailed literature is provided in each section.

The application of near-infrared spectroscopy (NIRS), widely used in the agricultural, food, and pharmaceutical industries, is extended to the measurement of C, N, and P in seston from oligotrophic lakes at the Experimental Lakes Area, northwestern Ontario. Seston from 150-mL aliquots of lake water from several depths in two lakes on one mid-June sampling date was collected on Whatman GF/C filters and measured first by NIRS then analyzed by standard wet chemistry methods. Correlation between chemically measured and NIRS-predicted values gave coefficients of determination between 0.913 and 0.982. The rapid, low-cost, nonchemical, nondestructive, efficient analysis of C, N, and P in seston by NIRS is feasible.

Activated sludge process monitoring through in situ near-infrared spectral analysis

Meat and meat products are popular foods due to their balanced nutritional nature and their availability in a variety of forms. In recent years, due to an increase in the consumer awareness regarding product quality and authenticity of food, rapid and effective quality control systems have been sought by meat industries. Near-Infrared (NIR) spectroscopy has been identified as a fast and cost-effective tool for estimating various meat quality parameters as well as detecting adulteration. This review focusses on the on/inline application of single and multiprobe NIR spectroscopy for the analysis of meat and meat products starting from the year 1996 to 2017. The article gives a brief description about the theory of NIR spectroscopy followed by its application for meat and meat products analysis. A detailed discussion is provided on the various studies regarding applications of NIR spectroscopy and specifically for on/inline monitoring along with their advantages and disadvantages. Additionally, a brief description has been given about the various chemometric techniques utilized in the mentioned studies. Finally, it discusses challenges encountered and future prospects of the technology. It is concluded that, advancements in the fields of NIR spectroscopy and chemometrics have immensely increased the potential of the technology as a reliable on/inline monitoring tool for the meat industry.

Shaoxing rice wine (also called Shaoxing wine) is the most well-known Chinese rice wine in China. The common fraudulent practice in the commercialization of Chinese rice wine is to sell wines from different geographical origins under the denomination of Shaoxing rice wine. In this study, the use of near-infrared (NIR) spectroscopy combined with chemometrics as a rapid tool for the discrimination of Chinese rice wine from three geographical origins (“Fujian”, “non-Shaoxing”, “Shaoxing”) has been preliminarily investigated. NIR spectra were collected in transmission mode in the wavelength range of 800–2,500 nm. Discriminant models were developed by principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least-squares analysis (DPLS). The chemical properties of Chinese rice wine were also investigated to find out the difference between samples from three varied origins. The results showed that good classification could be obtained after spectral pre-treatment. The percentage of samples correctly classified by both DA and DPLS methods in calibration and validation set was 97.2% and 100%, respectively. The results demonstrated that NIR could be used as a simple and rapid technique to distinguish Shaoxing wines from non-Shaoxing wines and Fujian wines. To further validate the ability of NIR spectroscopy, more samples should be incorporated to build a more robust model.

Lignin and cellulose content differences in roots of different cotton cultivars associated with different levels of Fusarium wilt race 4 (FOV4) resistance-response

Shade effect on carbohydrates dynamics and stem strength of soybean genotypes

Near-infrared spectroscopy (NIRS) is a method for noninvasive estimation of cerebral hemodynamic changes. Principal component analysis (PCA) and independent component analysis (ICA) can be used for decomposing a set of signals to underlying components. Our objective is to determine whether PCA or ICA is more efficient in identifying and removing scalp blood flow interference from multichannel NIRS signals. Concentration changes of oxygenated (HbO(2)) and deoxygenated (HbR) hemoglobin are measured on the forehead with multichannel NIRS during hyper- and hypocapnia. PCA and ICA are used separately to identify and remove signal contribution from extracerebral tissue, and the resulting estimates of cerebral responses are compared to the expected cerebral responses. Both methods were able to reduce extracerebral contribution to the signals, but PCA typically performs equal to or better than ICA. The improvement in 3-cm signal quality achieved with both methods is comparable to increasing the source-detector separation from 3 to 5 cm. Especially PCA appears to be well suited for use in NIRS applications where the cerebral activation is diffuse, such as monitoring of global cerebral oxygenation and hemodynamics. Performance differences between PCA and ICA could be attributed primarily to different criteria for identifying the surface effect.

The first and the second generation of improved maritime pine (Pinus pinaster) have produced gains of up to 30% in stem volume, a reduction of final harvesting age by 10 years and a considerable improvement of stem straightness. The third generation will include wood quality traits for different end-uses, including pulp and fibre properties. To facilitate this goal, near infrared spectroscopy (NIRS) was used to estimate chemical composition with regard to lignin, cellulose, hemicelluloses, and extractive content, and lignin quality with regard to 4-hydroxy-phenylpropane/guaiacylpropane ratio. A total of 960 samples were investigated which were collected from a large number of trees (belonging to 80 families obtained by crossing 18 mothers and 20 fathers; there are 12 trees per family). Good calibration data was obtained between NIRS and wet chemistry methods (R2 values higher than 0.9 and good precision of prediction). To complete the NIRS work, kraft cookings in small scale, fibre morphology and microdensitometry investigations were also conducted. Genetic calculations indicated that for a 1% rate of selection on mothers and fathers, genetically induced changes are possible with lignin content (−3.8%), cellulose content (+1.3%), pulp yield (+1.8%), fibre length in pulps (+0.17 mm) and wood density (+50 kg m−3).

Applicability of near-infrared spectroscopy in the monitoring of film coating and curing process of the prolonged release coated pellets