Accurately　determining　the　soluble　solid　content　(SSC),　titratable　acidity　(TA),　and　firmness　of　multiple　pear　varieties　is　crucial　for　enhancing　their　market　appeal.　Traditional　methods　using　spectral　techniques　require　laborious　and　trial-and-error　preprocessing,　feature-wavelength　selection,　and　model-establishment　process.　Additionally,　multiple　independent　models　need　to　be　trained　to　predict　different　attributes.　In　this　study,　we　developed　a　multi-task　convolutional　neural　network　(MCNN)　model　based　on　the　whale　optimization　algorithm　(WOA)　to　establish　a　global　model　for　simultaneously　determination　of　SSC,　TA,　and　firmness　of　three　pear　varieties.　Spectral　data　in　the　range　of　300–900　nm　were　collected　from　individual　pear　samples　using　a　self-designed　transmission　spectra-acquisition　device.　We　established　and　compared　an　individual-variety　prediction　model　and　a　global　model　using　three　pear　varieties.　The　optimized　MCNN　model　achieved　optimal　prediction　accuracies　for　the　SSC　(Rv2　=　0.977,　residual　prediction　deviation　[RPD]　=　5.414),　TA　(Rv2　=0.970,　RPD　=　5.057),　and　firmness　(Rv2　=0.978,　RPD　=　6.326).　Combing　the　global-modeling　strategy　with　optimized　MCNN　architecture　enhanced　the　model＇s　robustness　to　variety　variation　and　improved　the　accuracy　in　detecting　pear-quality　traits.　This　model　provides　a　new,　convenient,　and　automated　method　for　end-to-end　and　simultaneous　predictions　of　multiple　quality　indicators　in　fruit　without　any　further　processing　of　raw　spectra.　©　2024　Elsevier　B.V.