## 4 Conclusions  
In conclusion, this study investigated the feasibility of transferring NIR spectral models from one type of fruit to another. The results showed that the transferability of NIR models between fruits is challenging due to the spectral variations caused by various factors such as temperature change, humidity change, instrument drift, sample heterogeneity, and background noise. However, several methods, including spectral preprocessing, global modeling, and calibration transfer without standard, can improve the transferability of NIR models between fruits.  
The two case studies conducted in this study, namely strawberry-to-grape transfer and strawberry-to-apple transfer, demonstrated that the performance of NIR models built for one fruit type could be degraded when applied to another fruit type. The comparison of various methods showed that global modeling approaches generally have acceptable results for all fruits but degrade the performance of already established models. Calibration transfer without standard methods, such as semi-supervised parameter-free calibration enhancement and PLS correction, can correct the prediction bias when the model built for one fruit is applied to another with similar data requirements as global modeling.  
This study provides valuable insights for improving the practical application of NIR spectroscopy in fruit detection by investigating the feasibility of transferring NIR spectral models between fruits. The strong penetrating power of NIR spectroscopy makes it suitable for analyzing the quality of thin-skinned fruits, including apples, strawberries, pears, and other similar fruits. This non-destructive and efficient technology can effectively assess fruit quality, which is of great importance to fruit industry stakeholders. Future research should explore more effective approaches to improving the transferability of NIR models between fruits, such as incorporating more diverse reference datasets, developing more  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Prediction Set** & **RMSEP (\({}^{\circ}\)Brix)** & **Rp** \\ \hline Strawberry & Strawberry & 0.41 & 0.95 \\ Strawberry & Grape & 2.40 & 0.74 \\ Strawberry & Apple & 4.90 & 0.12 \\ SS-PFCE & Grape & 1.99 & 0.84 \\ SS-PFCE & Apple & 1.14 & 0.65 \\ PLS Correction & Grape & 1.77 & 0.87 \\ PLS Correction & Apple & 1.22 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction of the strawberry model without and with calibration transfer.  
Figure 6: Prediction of transferred models by SS-PFCE (**A**) and PLS correction (**B**).  
robust calibration transfer methods, and exploring the use of artificial intelligence and machine learning techniques.  
Conceptualization, C.G. and J.Z.; Methodology, J.Z.; Software, X.S.; Validation, C.G., [PERSON] and X.S.; Formal Analysis, X.S.; Investigation, J.Z.; Resources, C.G.; Data Curation, C.G.; Writing--Original Draft Preparation, C.G.; Writing--Review and Editing, X.S.; Visualization, W.C.; Supervision, X.S.; Project Administration, X.S.; Funding Acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 21775076 and 22004022).  
Not applicable.  
The data presented in this study are openly available upon request.  
The authors declare no conflict of interest.