Rapid and accurate detection of protein and starch contents is important to ensure maize quality. However, existing methods for rapidly detecting protein and starch contents often suffer from drying samples to improve accuracy but significantly increase the overall detection time. To bridge this research gap, this study aims to integrate spectral and image features of visible near-infrared hyperspectral imaging for rapid determination of protein and starch contents with high accuracy under the effect of water on maize kernels. First, the spectral information at 399.75–1005.80 nm, color (the first, second, and third-order moments of H, S, and V color channels), and texture (contrast, correlation, energy, homogeneity, and entropy) features data were extracted. Then, 40 prediction models, including various preprocessing methods and modeling algorithms, partial least squares regression (PLSR), support vector regression (SVR), and extreme learning machine (ELM), were established and compared. Furthermore, the characteristic wavelengths are selected by successive projection algorithm (SPA) and uninformative variables elimination (UVE), and further fused with color and texture features data to improve model accuracy. The results showed that the best prediction models of both protein and starch contents were built based on fusion data. The standard normal transformation (SNV)-SPA-Color-ELM-Protein model and multiplicative scatter correction (MSC)-UVE-Texture-ELM-Starch model were optimal, which can achieve rapid and accurate detection in maize kernels under the effect of water. 

Integration of spectral and image features of hyperspectral imaging for quantitative determination of protein and starch contents in maize kernels

Amylose content (AC) is critical in evaluating starch properties, significantly influencing the food industry and human nutrition. Although amylose is not completely linear, its unique structure makes it a key research focus across various scientific fields. Understanding amylose's structural and functional properties is essential for its applications in medical, nutritional, and industrial sectors. Accurate determination of AC, from simple qualitative assessments to precise quantitative measurements, is vital for effectively processing and using starch-rich products. The choice of AC determination method depends on the specific application and the required accuracy and detail. This review summarizes amylose's structural and functional characteristics and recent advancements in qualitative and quantitative AC determination techniques. It also provides insights into future trends and prospects for these technologies, emphasizing the need for more rapid, convenient, accurate, and customizable methods. In conclusion, advancements in amylose determination should enhance accuracy, speed, and ease of use to improve quality control and applications across various sectors while expanding our understanding of amylose and its functionalities. 

Evaluation of amylose content: Structural and functional properties, analytical techniques, and future prospects

Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (Rp: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion.

Advanced chemometrics toward robust spectral analysis for fruit quality evaluation

The infrared spectroscopy (IR) signal of protein is prone to being covered by impurity signals, and the accuracy of the secondary structure content calculated using spectral data is poor. To tackle this challenge, a rapid high-precision quantitative model for protein secondary structure was proposed. Firstly, a two-dimensional correlation calculation was performed based on 60 groups of soybean protein isolates (SPI) infrared spectroscopy data, resulting in a two-dimensional correlation infrared spectroscopy (2DCOS-IR). Subsequently, the optimal characteristic bands of the four secondary structures were extracted from the 2DCOS-IR. Ultimately, partial least squares (PLS), long short-term memory (LSTM), and bidirectional long short-term memory (BILSTM) algorithms were used to model the extracted characteristic bands and predict the content of SPI secondary structure. The findings suggested that BILSTM combined with 2DCOS-IR model (2DCOS-BILSTM) exhibited superior predictive performance. The prediction sets for α-helix, β-sheet, β-turn, and random coil were designated as 0.9257, 0.9077, 0.9476, and 0.8443, respectively, and their corresponding RMSEP values were 0.26, 0.48, 0.20, and 0.15. This strategy enhances the precision of IR and facilitates the rapid identification of secondary structure components within SPI, which is vital for the advancement of protein industrial production. 

Rapid quantitative analysis of soybean protein isolates secondary structure by two-dimensional correlation infrared spectroscopy through pH perturbation

The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review

A Novel Method Based on Multi-Molecular Infrared (MM-IR) AlexNet for Rapid Detection of Trace Harmful Substances in Flour

This paper builds a real-time infrared target detection system with a visual positioning function based on FPGA, a visual camera, and an infrared camera. Firstly, the whole system is built based on FPGA. The design and implementation of the infrared image acquisition module, a data storage module, SD card module, and image display module are completed. Ping pong operation is used for the data storage modules to realize video stream transmission. The experiment results show that the system built in this paper can collect and display the target in real-time. To improve the system performance, several image processing algorithms are proposed, including an improved median filtering algorithm, linear transformation, and Laplacian sharpening algorithm; a combined algorithm of histogram equalization, Gamma transform, and Laplacian sharpening; a target detection algorithm combined with threshold segmentation and a background difference algorithm; and a visual localization algorithm. Software simulation and FPGA hardware implementation results show the effectiveness of the proposed algorithms.

An FPGA-based real-time infrared target detection system with visual image positioning

Indoor fire sensing robot could become the best choice to detect irregularity features in a complex situation with help of nascent visual technologies. This article works on improvement computational time and accuracy of video fire detection (VFD) using on those robots. The profile of flame is first identified by flame luminance and chrominance using the proposed VFD network. A more efficient layer aggregation connection is designed to improve detection accuracy. The proposed feature pyramid layer combining ghost convolution and cross-connection improves the feature extraction ability. Improvements in reparameterized blocks with residual connections increase inference speed. The irrigation feature of fast burning fire flame in the incipient, growth, full-developed and decay stages are fully detected with analysis of the proposed visualized Heat Release Rate (vHRR) with spark, flicker, and other dynamic disturbances involved. Compared with high-performance methods, the average detection accuracy of this method on public datasets and self-built datasets is improved by 3.5% and 2.3%. Five indoor representative scenarios illustrate that our method achieves a good balance between the detection accuracy and speed of flame trends. In the future, the model could be deployed in lightweight, fast-moving indoor sensor robots for fire detection in hazardous environments.

A Fast Video Fire Detection of Irregular Burning Feature in Fire-Flame Using in Indoor Fire Sensing Robots

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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review

A Novel Method Based on Multi-Molecular Infrared (MM-IR) AlexNet for Rapid Detection of Trace Harmful Substances in Flour

An FPGA-based real-time infrared target detection system with visual image positioning

A Fast Video Fire Detection of Irregular Burning Feature in Fire-Flame Using in Indoor Fire Sensing Robots