There are two kinds of tutorial articles: those that provide a primer on an established topic and those that let us in on the ground floor of something of emerging importance. The first type of tutorial can have a noted expert who has been gracious (and brave) enough to write a field guide about a particular topic. The other sort of tutorial typically involves researchers who have each been laboring on a topic for some years. Both sorts of tutorial articles are very much desired. But we, as an editorial board for both Systems and Transactions, know that there has been no logical place for them in the AESS until this series was started several years ago. With these tutorials, we hope to continue to give them a home, a welcome, and provide a service to our membership. We do not intend to publish tutorials on a regular basis, but we hope to deliver them once or twice per year. We need and welcome good, useful tutorial articles (both kinds) in relevant AESS areas. If you, the reader, can offer a topic of interest and an author to write about it, please contact us. Self-nominations are welcome, and even more ideal is a suggestion of an article that the editor(s) can solicit. All articles will be reviewed in detail. Criteria on which they will be judged include their clarity of presentation, relevance, and likely audience, and, of course, their correctness and scientific merit. As to the mathematical level, the articles in this issue are a good guide: in each case the author has striven to explain complicated topics in simple-well, tutorial-terms. There should be no (or very little) novel material: the home for archival science is the Transactions Magazine, and submissions that need to be properly peer reviewed would be rerouted there. Likewise, articles that are interesting and descriptive, but lack significant tutorial content, ought more properly be submitted to the Systems Magazine.

What is a tutorial

Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1541-4337.12314

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review.

https://digital.csic.es/bitstream/10261/184124/1/Alehosseini_Food%20Hyd_2019.pdf

Electrospun curcumin-loaded protein nanofiber mats as active/bioactive coatings for food packaging applications

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.

/pdf/hyperspectral-imaging-for-mapping-of-total-nitrogen-spatial-2o8f2el904.pdf

Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant.

Comparison of grating-based near-infrared (NIR) and Fourier transform mid-infrared (ATR-FT/MIR) spectroscopy based on spectral preprocessing and wavelength selection for the determination of crude protein and moisture content in wheat

Determination of tea polyphenols content by infrared spectroscopy coupled with iPLS and random frog techniques

Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms

Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration (n = 113) and prediction (n = 56). Six different pretreatment methods were compared to determine the optimal pretreatment method for analysis. The wavelength variables selected by regression coefficient analysis, successive projections algorithm, and genetic algorithm–partial least squares analysis (GAPLS) were used as the inputs of partial least-squares analysis, multiple linear regression (MLR), and least-squares support vector machine. The performances of these calibration methods were also compared to determine the best model. The results indicated that GAPLS-MLR obtained the highest precision with a correlation coefficient of prediction r
 pre = 0.9223, root mean square of prediction RMSEP = 0.0751 g/100 g, and coefficient of variation CV = 5.15 %. The overall results showed that NIR was feasible for the determination of aspartic acid in fermented Cordyceps powder and GAPLS could perform well with less input dimension and computation complexity in the aspartic acid estimation.

Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods

Raman spectroscopy was first adopted for rapid detecting a hazardous substance of lead chrome green in tea, which was illegally added to tea to disguise as high-quality. 160 samples of tea infusion with different concentrations of lead chrome green were prepared for Raman spectra acquirement in the range of 2804 cm(-1)-230 cm(-1) and the spectral intensities were calibrated with relative intensity standards. Then wavelet transformation (WT) was adopted to extract information in different time and frequency domains from Raman spectra, and the low-frequency approximation signal (ca4) was proved as the most important information for establishment of lead chrome green measurement model, and the corresponding partial least squares (PLS) regression model obtained good performance in prediction with Rp and RMSEP of 0.936 and 0.803, respectively. To further explore the important wavenumbers closely related to lead chrome green, successive projections algorithm (SPA) was proposed. Finally, 8 characteristic wavenumbers closely related to lead chrome green were obtained and a more convenient and fast model was also developed. These results proved the feasibility of Raman spectroscopy for nondestructive detection of lead chrome green in tea quality control.

/pdf/nondestructive-detection-of-lead-chrome-green-in-tea-by-3efxbmehzo.pdf

Nondestructive detection of lead chrome green in tea by Raman spectroscopy.

The invention discloses a detection method for distribution of carotene in Isochrysis galbana based on Raman spectroscopy. The detection method comprises the following steps: acquiring original Raman spectroscopic information of a living Isochrysis galbana liquid sample by using a Raman spectrometer; with carotene in pigment as an example, acquiring the spectral line of a standard carotene product by using the Raman spectrometer; preprocessing obtained original Raman data of the Isochrysis galbana liquid sample and identifying spectral peak positions of corresponding Raman spectral peaks of the Isochrysis galbana liquid sample on the basis of the spectral line of the standard carotene product; and scanning the Isochrysis galbana liquid sample in a certain area and subjecting Raman intensity values of characteristic spectral peaks corresponding to carotene to integration so as to obtain a chemical image of distribution of carotene in Isochrysis galbana liquid. The detection method provided by the invention overcomes the problems of need for dyeing or complex chemical treatment of a sample, complicated operation, consumption of time and labor, etc. of conventional detection methods.

Luo Liubin

Papers

Determination of tea polyphenols content by infrared spectroscopy coupled with iPLS and random frog techniques

Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms

Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods

Nondestructive detection of lead chrome green in tea by Raman spectroscopy.

Detection method for distribution of carotene in Isochrysis galbana based on Raman spectroscopy