Review of experimental techniques for high rate deformation and shock studies

Until very recently, handheld spectrometers were the domain of major analytical and security instrument companies, with turnkey analyzers using spectroscopic techniques from X-ray fluorescence (XRF) for elemental analysis (metals), to Raman, mid-infrared, and near-infrared (NIR) for molecular analysis (mostly organics). However, the past few years have seen rapid changes in this landscape with the introduction of handheld laser-induced breakdown spectroscopy (LIBS), smartphone spectroscopy focusing on medical diagnostics for low-resource areas, commercial engines that a variety of companies can build up into products, hyphenated or dual technology instruments, low-cost visible-shortwave NIR instruments selling directly to the public, and, most recently, portable hyperspectral imaging instruments. Successful handheld instruments are designed to give answers to non-scientist operators; therefore, their developers have put extensive resources into reliable identification algorithms, spectroscopic libraries or databases, and qualitative and quantitative calibrations. As spectroscopic instruments become smaller and lower cost, “engines” have emerged, leading to the possibility of being incorporated in consumer devices and smart appliances, part of the Internet of Things (IOT). This review outlines the technologies used in portable spectroscopy, discusses their applications, both qualitative and quantitative, and how instrument developers and vendors have approached giving actionable answers to non-scientists. It outlines concerns on crowdsourced data, especially for heterogeneous samples, and finally looks towards the future in areas like IOT, emerging technologies for instruments, and portable hyphenated and hyperspectral instruments.

https://journals.sagepub.com/doi/pdf/10.1177/0003702818809719

EXPRESS: Portable Spectroscopy:

This review highlights the limited amount of research conducted regarding the optimum particle size of different feeds for efficient poultry production. The current industry practice of using highl...

Feed particle size: Implications on the digestion and performance of poultry

Physical and nutritional effects of pelleting of broiler chicken diets made from wheat ground to different coarsenesses by the use of roller mill and hammer mill

Literature on the crispy/crunchy behavior of cellular solid foods with a crust is discussed. The emphasis is on products with a dry crispy or crunchy crust as bread and various snacks and especially on mesoscopic and macroscopic aspects. Successively, the sensory sensations involved, the mechanical and fracture behavior of crispy/crunchy products, morphological aspects, and the relation between crispy and crunchy behavior and mobility of the macro-molecules and plasticizer (primarily water) involved, are discussed. Finally, some ideas for an integrated approach of crispy/crunchy behavior of cellular solid foods with a dry crust will be discussed.

Crispy/crunchy crusts of cellular solid foods: a literature review with discussion

Hyperspectral imaging (HSI) combines spectroscopy and imaging, providing information about the chemical properties of a material and their spatial distribution. It represents an advance of traditional Near-Infrared (NIR) spectroscopy. The present work reviews the most recent applications of NIR spectroscopy for cereal grain evaluation, then focused on the use of HSI in this field. The progress of research from ground material to whole grains and single kernels is detailed. The potential of NIR-based methods to predict protein content, sprout damage and α-amylase activity in wheat and barley is shown, in addition to assessment of quality parameters in other cereals such as rice, maize and oats, and the estimation of fungal infection. This analytical technique also offers the possibility to rapidly classify grains based on properties such as variety, geographical origin, kernel hardness, etc. Further applications of HSI are expected in the near future, for its potential for rapid single-kernel analysis.

https://www.tandfonline.com/doi/pdf/10.1080/05704928.2018.1425214

Near infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains

Single Kernel Wheat Hardness and Fracture Properties in Relation to Density and the Modelling of Fracture in Wheat Endosperm

https://nottingham-repository.worktribe.com/preview/961238/1-s2.0-S0260877418300219-main.pdf

Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging

Material properties of semi-sweet biscuits for finite element modelling of biscuit cracking

Author Summary The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN) method, which is a batch analysis. Hyperspectral imaging (HSI) can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR) region (~900–2500 nm) and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i) application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel); ii) calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 s; RMSEP ~ 63 s) compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA) was also applied to segregate wheat kernels into low ( 250 s) HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.

/pdf/application-of-calibrations-to-hyperspectral-images-of-food-45tm8msvzz.pdf

M.B. Whitworth

Papers

Near infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains

Single Kernel Wheat Hardness and Fracture Properties in Relation to Density and the Modelling of Fracture in Wheat Endosperm

Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging

Material properties of semi-sweet biscuits for finite element modelling of biscuit cracking

Application of calibrations to hyperspectral images of food grains: example for wheat falling number