Let D(G) and Tr(G)\documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{Tr}}(G)$$\end{document} be, respectively, the distance matrix and the diagonal matrix of the vertex transmissions of a connected graph G. The generalized distance matrix is defined as Tα(G)=αTr(G)+(1-α)D(G)\documentclass[12pt]{minimal}
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\begin{document}$$T_{\alpha }(G)=\alpha {\mathrm{Tr}}(G)+(1-\alpha )D(G)$$\end{document}, where 0≤α≤1\documentclass[12pt]{minimal}
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\begin{document}$$ 0\le \alpha \le 1$$\end{document}. If ∂1≥∂2≥⋯≥∂n\documentclass[12pt]{minimal}
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\begin{document}$$\partial _{1}\ge \partial _{2}\ge \cdots \ge \partial _{n}$$\end{document} are the eigenvalues of Tα(G)\documentclass[12pt]{minimal}
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\begin{document}$$T_{\alpha }(G)$$\end{document}, the generalized distance spread (or Tα\documentclass[12pt]{minimal}
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\begin{document}$$T_{\alpha }$$\end{document}-spread) is defined as STα(G)=∂1-∂n\documentclass[12pt]{minimal}
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\begin{document}$$S_{T_{\alpha }}(G)=\partial _1-\partial _n$$\end{document}. In this paper, we obtain an upper bound for the smallest generalized distance eigenvalue ∂n\documentclass[12pt]{minimal}
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\begin{document}$$\partial _{n}$$\end{document} in terms of different graph parameters. In particular, we show that this upper bound is better than the upper bound obtained by Cui et al. (Linear Algebra Appl 563:1–23, 2019). As an application to this upper bound, we obtain a lower bound for the generalized distance spread STα(G)\documentclass[12pt]{minimal}
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\begin{document}$$S_{T_{\alpha }}(G)$$\end{document} and discuss some of its consequences. Furthermore, we obtain a lower bound for STα(G)\documentclass[12pt]{minimal}
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\begin{document}$$S_{T_{\alpha }}(G)$$\end{document} in terms of the chromatic number χ\documentclass[12pt]{minimal}
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\begin{document}$$\chi $$\end{document} of the graph G. Also, we discuss the nature of Tα\documentclass[12pt]{minimal}
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\begin{document}$$T_{\alpha }$$\end{document}-spread STα(G)\documentclass[12pt]{minimal}
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\begin{document}$$S_{T_{\alpha }}(G)$$\end{document} under some graph operations.
