Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics

Chen, Rui; Li, Shaoqun; Cao, Huijuan; Xu, Tongguang; Bai, Yanchang; Li, Zhanming; Leng, Xiaojing; Huang, Yue

doi:10.1016/j.foodchem.2023.137931

Cited by 14 publications

(1 citation statement)

References 30 publications

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“…SG preprocessing ensures the preservation of signal shape and width while filtering out unwanted noise components [21]. SNV preprocessing corrects spectral errors caused by scattering effects in measured sample spectra, thereby eliminating influences from changes in optical range or sample dilution on spectral response [22,23]. In practical spectrum analysis, multiple interferences are often encountered; therefore, combining various preprocessing methods helps mitigate these interferences for improved model accuracy and stability [24].…”

Section: Spectral Preprocessingmentioning

confidence: 99%

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Xu,

Fu,

Gao

et al. 2024

Agriculture

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The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm−1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10−8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis.

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Section: Spectral Preprocessingmentioning

confidence: 99%

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Xu,

Fu,

Gao

et al. 2024

Agriculture

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Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting

Naskar,

Sing,

Banerjee

et al. 2024

Phytochemical Analysis

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IntroductionGinger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non‐invasive quality and traceability assessment techniques ensure a sustainable value chain.ObjectiveThe objective of this study is the development of suitable machine learning models to estimate the concentration of 6‐gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near‐infrared spectrometry.MethodsSamples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6‐gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint‐based traceability identification employed principal component analysis and t‐distributed stochastic neighbor embedding (t‐SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints.ResultsThe standard normal variate pretreated spectral data over the wavelength region of 1,100–1,250 nm and 1,325–1,550 nm showed the optimal calibration model with root mean square error of calibration and R2C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t‐SNE performed better clustering of samples based on geographical location, which was independent of gingerol content.ConclusionThe developed NIR spectroscopic model for Indian ginger samples predicts the 6‐gingerol content and provides geographical traceability‐based identification to ensure a sustainable value chain, which can promote efficiency, cost‐effectiveness, consumer confidence, sustainable sourcing, traceability, and data‐driven decision‐making.

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Detection technologies, and machine learning in food: Recent advances and future trends

He,

Huang,

Wang

2024

Food Bioscience

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Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics

Cited by 14 publications

References 30 publications

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting

Detection technologies, and machine learning in food: Recent advances and future trends

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