Publications by authors named "Shiqiang Jia"

The effects of varieties, producing areas, ears, and ear positions of maize on near-infrared (NIR) spectra were investigated to determine the factors causing the differences in NIR fingerprints of maize varieties. A total of 130 inbred lines were grown in two regions in China, and 12,350 kernel samples were analyzed through NIR spectroscopy. Spectral differences among varieties, producing areas, ears, and ear positions were determined and compared on the basis of pretreated spectra.

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Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene.

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Article Synopsis
  • The article investigates the use of Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectroscopy for identifying the purity of maize hybrids.
  • Both techniques were compared, showing that NIR achieved a 100% accuracy for one hybrid and 90% for another, but its results were sensitive to seed placement.
  • In contrast, NIT showed a consistent 98% accuracy for both hybrids and demonstrated more reliability for analyzing individual seed kernels.
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This paper explored the relationship among genetic distances, NIR spectra distances and NIR-based identification model performance of the seeds of maize inbred lines. Using 3 groups (total 15 pairs) of maize inbred lines whose genetic distaches are different as experimental materials, we calculates the genetic distance between these seeds with SSR markers and uses Euclidean distance between distributed center points of maize NIR spectrum in the PCA space as the distances of NIR spectrum. BPR method is used to build identification model of inbred lines and the identification accuracy is used as a measure of model identification performance.

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It is generally accepted that near infrared reflectance spectroscopy (NIRS) can be used to identify variety authenticity of bare maize seeds. In practical, maize seeds are covered with seed coating agents. Therefore it's of huge significance to investigate the feasibility of identifying coated maize seeds by NIRS.

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Article Synopsis
  • - The study explored using hyperspectral imaging to distinguish between maize hybrid and its female parent by analyzing spectral data from images collected in the 871-1,699 nm range with 308 wavelengths.
  • - Different sample placements and environmental conditions were tested, showing that identification models maintained over 90% accuracy in correctly identifying the maize varieties.
  • - Key spectral bands (1,195-1,246 nm) were identified as most effective for distinguishing the hybrid from its parent, with models using these bands performing as well as those using a broader spectral range.
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In the present paper, the spectral measurements of maize population samples were researched so as to identify their authenticity. Diffuse reflectance and transmittance measure modes were used to collect spectral data of 8 maize varieties. DPLS-DA was used to compress pretreated data.

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Article Synopsis
  • Near infrared spectrum analysis is crucial for accurately identifying different maize varieties based on their spectral data.
  • Eight maize varieties were analyzed using diffuse transmittance measurements and various data processing methods, including PCA, ICA, PLS-DA, and wavelet transformation.
  • Among these methods, PLS-DA outperformed the others, resulting in a higher average correct recognition rate for identifying the maize varieties.
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Article Synopsis
  • The study explored different methods for analyzing maize seed samples using near-infrared spectral measurements to improve variety identification models.
  • It found that positioning the maize embryo towards the light source and using diffuse reflectance settings resulted in better model performance compared to other methods.
  • The best results were achieved using a small sample pool with the embryo facing the light source, achieving a 94.6% correct identification rate and 96.5% correct rejection rate for non-target varieties.
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