Non-Invasive Blood Glucose Estimation using Handheld Near Infra-Red Device

doi:10.35940/ijrte.c1004.1083s19

Cited by 7 publications

(1 citation statement)

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“…NIRS has gained wide acceptance of industrial applications and research as a secondary non-destructive material fingerprinting. The applications can include medicalpharmaceutical fields [1][2][3], food/agricultural analysis [4][5][6][7], forensic [8], etc. NIRS is described as the hallmark for one of the most rapidly advancing analytical techniques over the last few decades [9].…”

Section: Introductionmentioning

confidence: 99%

Spectroscopy Data Calibration Using Stacked Ensemble Machine Learning

Mahmud Iwan Solihin,

Yuan,

Hong

et al. 2024

IIUMEJ

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Near infrared spectroscopy (NIRS) is a widely used analytical technique for non-destructive analysis of various materials including food fraud detection. However, the accurate calibration of NIRS data can be challenging due to the complexity of the underlying relationships between the spectral data and the target variables of interest. Ensemble learning, which combines multiple models to make predictions, has been shown to improve the accuracy and robustness of predictive models in various domains. This paper proposes stacking ensemble machine learning (SEML) for calibration of NIRS data with two levels of learning involved. Eight (8) spectroscopy datasets from public repository and previously published works by the authors are used as the case study. The model well generalized the data in the respective regression tasks with of at least »0.8 in the test samples and in the respective classification tasks with classification accuracy (CA) of at least »0.8 also. In addition, the proposed SEML can improve, or at least reach par with, the accuracy of individual base learners in both train and test samples for all cases of regression and classification datasets. It shows superior performance in test samples for both regression and classification datasets with respectively ranging from 0.86 to nearly 1 and CA ranging from 0.89 to 1. ABSTRAK: Spektroskopi inframerah dekat (NIRS) adalah teknik analitikal yang banyak digunakan bagi analisa pelbagai bahan tanpa merosakkan bahan termasuk ketika mengesan penipuan makanan. Walau bagaimanapun, kalibrasi yang tepat bagi data NIRS adalah sangat mencabar kerana hubungan antara data spektral dan pemboleh ubah sasaran yang ingin dikaji bersifat kompleks. Gabungan pembelajaran (Ensemble learning), iaitu gabungan pelbagai model bagi membuat prediksi, telah terbukti dapat meningkatkan ketepatan dan kecekapan model prediksi dalam pelbagai bentuk. Kajian ini mencadangkan Turutan Gabungan Pembelajaran Mesin (Stacking Ensemble Machine Learning ) (SEML), bagi teknik penentu ukuran data NIRS melibatkan dua tahap pembelajaran. Lapan (8) set data spektroskopi dari repositori awam dan kajian terdahulu oleh pengarang telah digunakan sebagai kes kajian. Model ini menggeneralisasi data dalam tugas regresi masing-masing sebanyak ?0.8 bagi sampel ujian dan pengelasan tugas masing-masing dengan ketepatan klasifikasi (CA) sekurang-kurangnya ?0.8. Tambahan, SEML yang dicadangkan ini dapat membantu, atau sekurang-kurangnya setanding dengan ketepatan individu dalam pembelajaran berkumpulan dalam kedua-dua sampel latihan dan ujian bagi semua kes set data regresi dan klasifikasi. Ia menunjukkan prestasi terbaik dalam sampel ujian bagi kedua-dua kumpulan set data regresi dan klasifikasi dengan masing-masing antara 0.86 hingga hampir 1 dan antara julat 0.89 hingga 1 bagi CA.

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