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High Spectral Spatial Frequency Domain Imaging (HSFDI) combines high spectral imaging and spatial frequency domain imaging techniques, offering advantages such as wide spectral range, non-contact, and differentiated imaging depth, making it well-suited for measuring the optical properties of agricultural products. The diffuse reflectance spectra of the samples at spatial frequencies of 0 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>mm</mi></mrow><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>d</mi><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula>) and 0.2 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>mm</mi></mrow><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>d</mi><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula>) were obtained using the three-phase demodulation algorithm. The pixel-by-pixel inversion was performed to obtain the absorption coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>μ</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math></inline-formula>) spectra and the reduced scattering coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>μ</mi></mrow><mrow><mi>s</mi></mrow><mrow><mo>′</mo></mrow></msubsup></mrow></semantics></math></inline-formula>) spectra of the pears. For predicting the SSC and firmness of the pears, these optical properties and their specific combinations were used as inputs for partial least squares regression (PLSR) modeling by combining them with the wavelength selection algorithm of competitive adaptive reweighting sampling (CARS). The results showed that <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>μ</mi><mi>a</mi></msub></mrow></semantics></math></inline-formula> had a stronger correlation with SSC, whereas <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>μ</mi></mrow><mrow><mi>s</mi></mrow><mrow><mo>′</mo></mrow></msubsup></mrow></semantics></math></inline-formula> exhibited a stronger correlation with firmness. Taking the plane diffuse reflectance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>d</mi><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula> as the comparison object, the prediction results of SSC based on both <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>μ</mi><mi>a</mi></msub></mrow></semantics></math></inline-formula> and the combination of diffuse reflectance at two spatial frequencies (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>d</mi></mrow></msub></mrow></semantics></math></inline-formula>) were superior (the best <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> of 0.90 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mi>E</mi><mi>P</mi></msub></mrow></semantics></math></inline-formula> of 0.41%). Similarly, in the prediction of firmness, the results of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>μ</mi></mrow><mrow><mi>s</mi></mrow><mrow><mo>′</mo></mrow></msubsup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>μ</mi></mrow><mrow><mi>a</mi></mrow></msub><mo>×</mo><msubsup><mrow><mi>μ</mi></mrow><mrow><mi>s</mi></mrow><mrow><mo>′</mo></mrow></msubsup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>d</mi><mn>1</mn></mrow></msub></mrow></semantics></math></inline-formula> were better than that of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>d</mi><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula> (the best <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> of 0.80 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mi>E</mi><mi>P</mi></msub></mrow></semantics></math></inline-formula> of 3.25%). The findings of this research indicate that the optical properties represented by HSFDI technology and their combinations can accurately predict the internal quality of pears, providing a novel technical approach for the non-destructive internal quality evaluation of agricultural products.